Digital sequence analysis of DNA methylation

ABSTRACT

The present invention relates to methods and compositions for determination of and uses of specific methylation patterns indicative of adenoma and carcinoma. In particular, the invention relates to analysis of defined CpG loci that are coordinately methylated in DNAs from cancer and adenoma samples, methods for identifying coordinately methylated loci, and methods of using analysis of coordinately methylated loci in one or more marker regions in the design of assays for adenoma and cancer.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent application Ser. No. 15/278,697, filed Sep. 28, 2016, now allowed, which is a divisional of U.S. patent application Ser. No. 13/364,978, filed Feb. 2, 2012, now U.S. Pat. No. 9,637,792, which claims the benefit of U.S. Provisional Patent Application Ser. No. 61/438,649, filed Feb. 2, 2011, each of which is incorporated by reference in its entirety.

DESCRIPTION OF THE SEQUENCE TEXT FILE SUBMITTED ELECTRONICALLY

The material in the Sequence Listing filed with this application on Oct. 28, 2019 as the ASCII text file entitled 31446-304_SEQUENCE_LISTING_ST25.TXT, created on Oct. 28, 2019 and having a file size of 4,532 bytes, is herein incorporated by reference.

FIELD OF THE INVENTION

The present invention relates to methods and compositions for determination of and uses of specific methylation patterns indicative of adenoma and carcinoma. In particular, the invention relates to analysis of defined CpG loci that are coordinately methylated in DNAs from cancer and adenoma samples, methods for identifying coordinately methylated loci, and methods of using analysis of coordinately methylated loci in one or more marker regions in the design of assays for adenoma and cancer having improved sensitivity and specificity.

BACKGROUND OF THE INVENTION

In higher order eukaryotes, DNA may be methylated at cytosines located 5′ to guanosine in CpG dinucleotides. This modification has important regulatory effects on gene expression, especially when involving CpG rich areas, known as CpG islands, often found in the promoter regions of genes. While approximately 75% of the CpG sites throughout the human genome are methylated, CpG sites within CpG islands are normally unmethylated, and aberrant methylation of CpG islands has been associated with certain diseases, including cancers. For example, CpG island hypermethylation is associated with transcriptional inactivation of defined tumor suppressor genes in human cancers, e.g., colorectal cancer. Therefore, detection of hypermethylated nucleic acid could indicate susceptibility or onset of various forms of cancers.

Despite indications suggesting a link between CpG island methylator phenotype (CIMP) and cancers (see, e.g., Baylin S B, et al., Adv Cancer Res 1998; 72:141-196 and Jones P A, et al., Nat Rev Genet 2002; 3:415-428), the idea that analysis of methylation status alone could be a useful diagnostic or prognostic tool has been controversial. As discussed by Issa, et al. in an editorial in Gastroenterology 179(3):2005, researchers had mixed results in confirming the link between CI,MP and cancers. Although CIMP was reportedly demonstrated in multiple other malignancies (Shen, I., et al. J Natl Cancer Inst 2002; 94:755-761; Garcia-Manero G, et al., Clin Cancer Res 2002; 8:2217-2224; Toyota M, et al., Blood 2001; 97:2823-2829; Ueki T, et al., Cancer Res 2000; 60:1835-1839; Toyota M, et al., Cancer Res 1999; 59:5438-5442; Strathdee G, et al., Am J Pathol 2001; 158:1121-1127; Abe M, et al., Cancer Res 2005; 65:828-834) and several groups confirmed the original findings using similar markers and technology (Whitehall V L, et al., Cancer Res 2002; 62:6011-6014; van Rijnsoever M, et al., Gut 2002; 51:797-802) other groups were not able to establish such links (Eads C A, et al., Cancer Res 2001; 61:3410-3418; Esteller M, et al., Cancer Res 2000; 60:129-133). As late as 2003, a publication concluded that all methylation events in colorectal cancer were related to aging rather than neoplasia (Yamashita K, et al., Cancer Cell 2003; 4:121-131).

The discrepant results have been attributed in part to the fact that it has been demonstrated that 70% to 80% of aberrant DNA methylation events in colorectal cancer are age-related (Toyota M, et al., Proc Natl Acad Sci USA 1999; 96:8681-8686) and that cancer-linked phenotypes are only clear when these are filtered out. It has also been noted that overly sensitive, non-quantitative methods can overestimate methylation and mask the distinctions between methylation that is associated with cancer and that which is not. Issa states that “methylation events (alone) may not provide the ideal universal cancer marker they were once thought to be because CIMP target genes will not be useful to screen for all colorectal cancers (many false negatives are predicted), and non-CIMP target genes will likely yield a high rate of false-positives because they are also methylated in normal appearing mucosa of older individuals without tumors” (Issa, et al., supra).

One approach to increase the clinical specificity of methylation analyses in cancer detection is to look at multiple marker genes. For example, Zou, et al., examined the methylation status of BMP3, EYA2, ALX4, and vimentin in cancer samples. While methylation levels were significantly higher in both cancer and adenoma than in normal epithelium, for each of the four genes, the sensitivity as determined by receiver operating curves was not significantly improved by combining any or all markers compared with the best single marker. (Zou, et al., Cancer Epidemiol Biomarkers Prev 2007; 16(12):2686).

Zou also looked at neoplasims showing methylation in more than one of the marker genes and found that co-methylation was frequent, with 72% of the cancers and 84% of the adenomas tested showing hypermehtylation in two or more of the genes. Zou reported that methylation of one or more of four (at least one), two or more of four, three or more of four, or four of four of these marker genes was noted in 88%, 72%, 53%, and 41% of 74 cancers and 98%, 84%, 60%, and 39% of 62 adenomas, compared with 24%, 7%, 3%, and 0% of 70 normal epithelia, respectively, demonstrating that although the assay gets progressively more specific as when more genes are included in the comethylation set, the sensitivity declines precipitously.

SUMMARY OF THE INVENTION

The present invention relates to the methods of identifying regions of specific genes and specific regions of genomic nucleic acid useful in the detection of methylation associated with colorectal cancer. Methods comprise, e.g., detecting methylated sequences in, for example, tissue biopsy, stool extract, or other body fluids with improved sensitivity and specificity. In preferred embodiments, the present invention provides methods of methylation analysis comprising identifying methylation loci showing advantageous methylation ratios when methylation in non-normal cells, e.g., cancer or adenoma cells is compared to background methylation in normal cells. In some embodiments, the present invention relates to methods of analyzing methylation at each of several loci in a set of possible methylation sites within a marker sequence, wherein the presence of methylation at all of the loci within the defined set of sites occurs more frequently in cancer and adenoma cells than in normal cells, such that a finding of methylation at all of the loci in the defined subset of loci in a sample is indicative of adenoma or cancer.

In some embodiments, the present invention provides a method of identifying a set of methylated CpG loci in a marker nucleic acid wherein methylation is indicative of adenoma, comprising:

a) determining the methylation status of a defined set of CpG loci in each of a plurality of individual copies of a marker nucleic acid from a plurality of normal samples;

b) determining the methylation status of said defined set of CpG loci in each of a plurality of individual copies of said marker nucleic acid from a plurality of non-normal (e.g., adenoma or cancer) samples to identify a defined subset of CpG loci from within said defined set,

wherein the percentage of individual copies of said marker nucleic acid from said plurality of normal samples that are methylated at all of said CpG loci in said defined subset is less than the percentage of individual copies of said marker nucleic acid from said plurality of non-normal samples that are methylated at all of said CpG loci in said defined subset, and wherein methylation at all of said CpG loci in said defined subset in said marker nucleic acid is indicative of a non-normal state, e.g., adenoma and/or cancer. In certain embodiments, the mean percentage of individual copies of the marker nucleic acid methylated at all loci in said defined set of CpG loci in said plurality of non-normal samples is greater than the mean percentage of individual copies of the marker nucleic acid methylated at all loci in said defined set of CpG loci in the plurality of normal samples. In preferred embodiments, the mean percentage of individual copies of the marker nucleic acid methylated at all loci in said defined set of CpG loci in the plurality of non-normal samples is at least one standard deviation, preferably two standard deviations, more preferably three standard deviations greater than the mean percentage of individual copies of said marker nucleic acid methylated at all loci in said defined set of CpG loci in said plurality of normal samples.

In some embodiments, the defined subset of CpG loci consists of the same loci in the defined set of CpG loci.

Determination of the methylation status of the set of CpG loci may be accomplished by any method known to those of skill in the art. In some embodiments, the method comprises treating DNA from the samples with bisulfite. Bisulfite modification treatment is described, e.g., in U.S. Pat. No. 6,017,704, the entire disclosure of which is incorporated herein by reference. In some embodiments, determining the methylation status of the defined set of CpG loci comprises digital analysis of each of a plurality of CpG loci in a plurality of individual copies of a marker nucleic acid. In some preferred embodiments, digital analysis comprise digital sequencing, and/or digital PCR.

In certain preferred embodiments, non-normal sample comprises an adenoma sample, and in particular preferred embodiments, comprises a colorectal adenoma sample. In some preferred embodiments, a non-normal sampled comprises a cancer sample, and in certain preferred embodiments, comprises a colorectal cancer sample.

The present invention provides methods of detecting cancer or adenoma in a sample, e.g., from a subject. In some embodiments, the present invention provides methods comprising determining the methylation status of each CpG locus in a defined subset of CpG loci in at least one cancer or adenoma marker nucleic acid molecule, wherein methylation at each of the CpG loci in the defined subset of CpG loci in the cancer or adenoma marker nucleic acid molecule is indicative of cancer or adenoma in the sample. In certain preferred embodiments, the defined subset comprises at least three CpG loci, while in some preferred embodiments, the defined subset comprises at least four CpG loci or at least five CpG loci.

In certain embodiments, the determining comprises analysis of the CpG loci in a nucleic acid detection assay configured to determine the methylation status of each of the loci in a single nucleic acid detection assay. In some preferred embodiments, the determining comprises analysis of the CpG loci in a nucleic acid detection assay configured to determine the methylation status of each of said loci in a single reaction mixture. In some embodiments, the nucleic acid detection assay comprises a primer extension assay. In certain preferred embodiments, the nucleic acid detection assay may comprise one or more of a nucleic acid amplification assay, a nucleic acid sequencing assay, a structure-specific cleavage assay, a 5′ nuclease cleavage assay, an invasive cleavage assay and/or a ligation assay.

The methods of the present invention are not limited to the analysis of a single cancer or adenoma marker nucleic acid. For example, in some embodiments, the methylation status of each CpG locus in a defined subset of CpG loci in at least one cancer or adenoma marker nucleic acid molecule comprises analysis of nucleic acid molecules from a plurality of cancer or adenoma markers. In some embodiments, the plurality of cancer or adenoma markers comprises at least three cancer or adenoma markers, while in some embodiments, the plurality comprises at least four cancer or adenoma markers. In some preferred embodiments, the cancer or adenoma markers and nucleic acid molecules are selected from the group comprising Vimentin, BMP3, Septin 9, TFPI2, 2 regions of LRAT, and EYA4 markers and nucleic acid molecules. In some embodiments, the assay methods of the present invention are combined with the analysis of one or more other cancer markers, such as fecal occult blood markers (e.g., hemoglobin, alpha-defensin, calprotectin, α1-antitrypsin, albumin, MCM2, transferrin, lactoferrin, and lysozyme).

In certain preferred embodiments of the method described herein, a cancer or adenoma marker nucleic acid molecule comprises a vimentin nucleic acid molecule, and in some particularly preferred embodiments, the defined subset of CpG loci in the vimentin nucleic acid molecule comprises loci 37, 40, and 45.

In certain preferred embodiments of the method described herein, a cancer or adenoma marker nucleic acid molecule comprises a BMP3 nucleic acid molecule, and in some particularly preferred embodiments, the defined subset of CpG loci in the BMP3 nucleic acid molecule comprises loci 34, 53, and 61.

In certain preferred embodiments of the method described herein, a cancer or adenoma marker nucleic acid molecule comprises a Septin9 nucleic acid molecule, and in some particularly preferred embodiments, the defined subset of CpG loci in said Septin9 nucleic acid molecule comprises loci 59, 61, 68, and 70.

In certain preferred embodiments of the method described herein, a cancer or adenoma marker nucleic acid molecule comprises a TFPI2 nucleic acid molecule and in some particularly preferred embodiments, the defined subset of CpG loci in said TFPI2 nucleic acid molecule comprises loci 55, 59, 63, and 67.

In certain preferred embodiments of the method described herein, a cancer or adenoma marker nucleic acid molecule comprises an EYA4 nucleic acid molecule, and in some particularly preferred embodiments, the defined subset of CpG loci in said EYA4 nucleic acid molecule comprises loci 31, 34, 37, and 44.

In certain preferred embodiments of the method described herein, the at least one cancer or adenoma marker or nucleic acid molecule comprises a plurality markers or nucleic acid molecules comprising Vimentin, BMP3, Septin9, and TFPI2 markers or nucleic acid molecules.

The present invention further provides methods of selecting a defined set of CpG loci in a marker nucleic acid wherein methylation is indicative of non-normal status, e.g., adenoma or cancer, comprising a) determining the methylation status of a plurality of CpG loci in each of a plurality of individual copies of a marker nucleic acid from a plurality of normal samples; b) determining the methylation status of the plurality of CpG loci in each of a plurality of individual copies of said marker nucleic acid from a plurality of non-normal (e.g., adenoma or cancer) samples; c) determining methylation ratios for each locus in the plurality of said CpG loci in the marker nucleic acid; and d) selecting a defined set of CpG loci in the marker nucleic acid, wherein the defined set of CpG loci comprises a plurality of CpG loci having advantageous methylation ratios correlating with non-normal status (e.g., adenoma or cancer).

In some embodiments, determining the methylation ratios comprises determining the ratio between the mean methylation at each of the plurality of CpG loci in the normal samples to the mean methylation at each corresponding CpG locus in said plurality of CpG loci in the non-normal samples. In preferred embodiments, the plurality of individual copies of a marker nucleic acid analyzed in the normal and non-normal (e.g., adenoma or cancer) samples comprises at least 10, preferably at least 100, more preferably at least 1000, still more preferably at least 10,000 and still more preferably at least 100,000 copies. The number of copies analyzed is not limited to these whole numbers, but may be any integer above about 10. The number of copies from different sample types, e.g., normal and non-normal need not be equal.

In certain preferred embodiments of the methods of selecting a defined set of CpG loci in a marker nucleic acid described herein, the plurality of normal and non-normal (e.g., adenoma or cancer) samples compared comprises at least 10, preferably at least 25, still more preferably at least 100 samples. The number of samples analyzed in not limited to these whole numbers, but may be any integer above about 10. The number of different samples of the different sample types, e.g., normal and non-normal, need not be equal.

In certain embodiments, the defined set of CpG loci comprises at least three CpG loci, preferably at least four CpG loci, more preferably at least five CpG loci.

Determination of the methylation status of the plurality of CpG loci may be accomplished by any method known to those of skill in the art, including those described in more detail, below. In some embodiments, the method comprises treating DNA from the samples with bisulfite. In some embodiments, determining the methylation status of the defined set of CpG loci comprises digital analysis of each of a plurality of CpG loci in a plurality of individual copies of a marker nucleic acid. In some preferred embodiments, digital analysis comprises digital sequencing, and/or digital PCR. Methods of preparing samples, e.g., stool samples, for analysis are also known in the art. See, e.g., U.S. Pat. Nos. 7,005,266; 6,303,304; 5,741,650; 5,952,178; and 6,268,136, each incorporated herein by reference.

Definitions

To facilitate an understanding of the present invention, a number of terms and phrases are defined below.

As used herein, the terms “digital sequencing” and “single molecule sequencing” are used interchangeably and refer to determining the nucleotide sequence of individual nucleic acid molecules. Systems for individual molecule sequencing include but are not limited to the 454 FLX™ or 454 TITANIUM™ (Roche), the SOLEXA™/Illumina Genome Analyzer (Illumina), the HELISCOPE™ Single Molecule Sequencer (Helicos Biosciences), and the SOLID™ DNA Sequencer (Life Technologies/Applied Biosystems) instruments), as well as other platforms still under development by companies such as Intelligent Biosystems and Pacific Biosystems.

As used herein, the term “background” as used in reference to methylation of a locus or region refers to methylation observed in a normal cell or sample at a nucleic acid locus or region that is generally unmethylated in normal cells. For example, CpG islands are generally considered unmethylated in normal human cells but methylation is not completely absent in the CpG islands of normal cells.

As used herein, “methylation” or “methylated,” as used in reference to the methylation status of a cytosine, e.g., in a CpG locus, generally refers to the presence or absence of a methyl group at position 5 of the cytosine residue (i.e., whether a particular cytosine is 5-methylcytosine). Methylation may be determined directly, e.g., as evidenced by routine methods for analysis of methylation status of cytosines, e.g., by determining the sensitivity (or lack thereof) of a particular C-residue to conversion to uracil by treatment with bisulfite. For example, a cytosine residue in a sample that is not converted to uracil when the sample is treated with bisulfite in a manner that would be expected to convert that residue if non-methylated (e.g., under conditions in which a majority or all of the non-methylated cytosines in the sample are converted to uracils) may generally be deemed “methylated”.

As used herein, the terms “digital PCR”, “single molecule PCR” and “single molecule amplification” refer to PCR and other nucleic acid amplification methods that are configured to provide amplification product or signal from a single starting molecule. Typically, samples are divided, e.g., by serial dilution or by partition into small enough portions (e.g., in microchambers or in emulsions) such that each portion or dilution has, on average, no more than a single copy of the target nucleic acid. Methods of single molecule PCR are described, e.g., in U.S. Pat. No. 6,143,496, which relates to a method comprising dividing a sample into multiple chambers such that at least one chamber has at least one target, and amplifying the target to determine how many chambers had a target molecule; U.S. Pat. No. 6,391,559; which relates to an assembly for containing and portioning fluid; and U.S. Pat. No. 7,459,315, which relates to a method of dividing a sample into an assembly with sample chambers where the samples are partitioned by surface affinity to the chambers, then sealing the chambers with a curable “displacing fluid.” See also U.S. Pat. Nos. 6,440,706 and 6,753,147, and Vogelstein, et al., Proc. Natl. Acad. Sci. USA Vol. 96, pp. 9236-9241, August 1999. See also US 20080254474, describing a combination of digital PCR combined with methylation detection.

As used herein, “sensitivity” as used in reference to a diagnostic assay, e.g., a methylation assay, refers to clinical sensitivity—the proportion of positive samples that give a positive result using a diagnostic assay. Sensitivity is generally calculated as the number of true positives identified by the assay, divided by the sum of the number of true positives and the number of false negatives determined by the assay on known positive samples. Similarly, the term “specificity” refers to the proportion or number of true negatives determined by the assay divided by the sum of the number of true negatives and the number of false positives determined by the assay on known negative sample(s).

As used herein in reference to diagnostic or analysis assays, the term “complementary” refers to different assays that, when used together, provide a more sensitive and/or specific result than can be provided by any one of the different assays used alone.

As used herein, the term “informative” or “informativeness” refers to a quality of a marker or panel of markers, and specifically to the likelihood of finding a marker (or panel of markers) in a positive sample.

The term “sample” as used herein is used in its broadest sense. For example, a sample suspected of containing a human gene or chromosome or sequences associated with a human chromosome may comprise a cell, chromosomes isolated from a cell (e.g., a spread of metaphase chromosomes), genomic DNA (in solution or bound to a solid support such as for Southern blot analysis), RNA (in solution or bound to a solid support such as for Northern blot analysis), cDNA (in solution or bound to a solid support) and the like.

As used herein, the term “CpG island” refers to a genomic DNA region that contains a high percentage of CpG sites relative to the average genomic CpG incidence (per same species, per same individual, or per subpopulation (e.g., strain, ethnic subpopulation, or the like). Various parameters and definitions for CpG islands exist; for example, in some embodiments, CpG islands are defined as having a GC percentage that is greater than 50% and with an observed/expected CpG ratio that is greater than 60% (Gardiner-Garden et al. (1987) J Mol. Biol. 196:261-282; Baylin et al. (2006) Nat. Rev. Cancer 6:107-116; Irizarry et al. (2009) Nat. Genetics 41:178-186; each herein incorporated by reference in its entirety). In some embodiments, CpG islands may have a GC content >55% and observed CpG/expected CpG of 0.65 (Takai et al. (2007) PNAS 99:3740-3745; herein incorporated by reference in its entirety). Various parameters also exist regarding the length of CpG islands. As used herein, CpG islands may be less than 100 bp; 100-200 bp, 200-300 bp, 300-500 bp, 500-750 bp; 750-1000 bp; 1000 or more bp in length. In some embodiments, CpG islands show altered methylation patterns relative to controls (e.g., altered methylation in cancer subjects relative to subjects without cancer; tissue-specific altered methylation patterns; altered methylation in stool from subjects with colorectal neoplasia (e.g., colorectal cancer, colorectal adenoma) relative to subjects without colorectal neoplasia). In some embodiments, altered methylation involves hypermethylation. In some embodiments, altered methylation involves hypomethylation.

As used herein, the term “CpG shore” or “CpG island shore” refers to a genomic region external to a CpG island that is or that has potential to have altered methylation patterns (see, e.g., Irizarry et al. (2009) Nat. Genetics 41:178-186; herein incorporated by reference in its entirety). CpG island shores may show altered methylation patterns relative to controls (e.g., altered methylation in cancer subjects relative to subjects without cancer; tissue-specific altered methylation patterns; altered methylation in stool from subjects with colorectal neoplasia (e.g., colorectal cancer, colorectal adenoma) relative to subjects without colorectal neoplasia). In some embodiments, altered methylation involves hypermethylation. In some embodiments, altered methylation involves hypomethylation. CpG island shores may be located in various regions relative to CpG islands (see, e.g., Irizarry et al. (2009) Nat. Genetics 41; 178-186; herein incorporated by reference in its entirety). Accordingly, in some embodiments, CpG island shores are located less than 100 bp; 100-250 bp; 250-500 bp; 500-1000 bp; 1000-1500 bp; 1500-2000 bp; 2000-3000 bp; 3000 bp or more away from a CpG island.

The term “target,” when used in reference to a nucleic acid detection or analysis method, refers to a nucleic acid having a particular sequence of nucleotides to be detected or analyzed, e.g., in a sample suspected of containing the target nucleic acid. In some embodiments, a target is a nucleic acid having a particular sequence for which it is desirable to determine a methylation status. When used in reference to the polymerase chain reaction, “target” generally refers to the region of nucleic acid bounded by the primers used for polymerase chain reaction. Thus, the “target” is sought to be sorted out from other nucleic acid sequences that may be present in a sample. A “segment” is defined as a region of nucleic acid within the target sequence. The term “sample template” refers to nucleic acid originating from a sample that is analyzed for the presence of a target.

As used herein, the term “locus” refers to a particular position, e.g., of a mutation, polymorphism, or a C residue in a CpG dinucleotide, within a defined region or segment of nucleic acid, such as a gene or any other characterized sequence on a chromosome or RNA molecule. A locus is not limited to any particular size or length, and may refer to a portion of a chromosome, a gene, functional genetic element, or a single nucleotide or basepair. As used herein in reference to CpG sites that may be methylated, a locus refers to the C residue in the CpG dinucleotide.

As used herein, the term “methylation ratio” refers to the amount or degree of methylation observed for particular methylation region or locus (e.g., a CpG locus in a marker gene or region) in a plurality of non-normal cells (e.g., cells in a particular disease state, such as cancerous or pre-cancerous cells) compared to the amount or degree of methylation observed for the same region or locus in a plurality of normal cells (e.g., cells that are not in the particular disease state of interest). For example, for a CpG locus showing mean methylation of 8.39889% in a sampling of normal cells and a mean methylation of 74.0771% in a sampling of a plurality of adenoma cells, a methylation ratio may be expressed as the ratio of the means determined for normal cells:adenoma cells, or 0.11348. A methylation ratio need not be expressed in any particular manner or by any particular calculation. By way of example and not limitation, the methylation ratio above may alternatively be expressed, e.g., as 8.39889:74.0771; 8.39889/74.0771; 74.0771:8.39889; as a calculated “fold methylation over background” 8.81987, etc.

As used herein, the term “advantageous methylation ratio” refers to a methylation ratio for a locus at which methylation correlates with a cellular status, e.g., a particular disease state (for example, normal, precancerous, cancerous) that, when compared to other methylation loci correlated with the same disease state, displays a higher percentage methylation in a population of non-normal cells compared to background levels of methylation at the same locus in a population of normal cells. In some instances, certain CpG loci e.g., within a methylation marker sequence, display a much greater signal-to-noise, i.e., degree in methylation compared to background than other loci in the same marker sequence. In other instances, certain disease-associated marker genes or regions display advantageous methylation ratios at some or all loci compared to the methylation ratios observed for some or all loci within another marker sequence.

As used herein, the term “coordinately methylated” is used in reference to methylation loci, e.g., CpG loci in a marker sequence, that exhibit a particular pattern of methylation that correlates with a cellular status, e.g., a particular disease state (for example, normal, precancerous, cancerous). In preferred embodiments, methylation loci that are all methylated in a manner correlated with a disease state may be deemed to be coordinately methylated in cells having that disease state. “Coordinate methylation” is not limited to situations in which all of the coordinated loci are methylated. Any pattern of methylation among a particular set of loci that correlates with a cellular status, including patterns in which all of the coordinate loci are methylated, patterns in which the loci exhibit a reproducible pattern of methylation and non-methylation, and patterns in which none of the loci within the set are methylated are all included within the meaning of “coordinately methylated.”

As used herein, the term “coordinate methylation analysis” is used interchangeably with “multimethylation analysis” and refers to an assay in which the methylation statuses of a plurality of individual methylation loci in a marker sequence, e.g., CpG loci, are determined together. In preferred embodiments, coordinate methylation analysis is performed using a digital/single copy method (e.g., digital sequencing) or an assay method configured to interrogate all of the selected CpG loci on each molecule tested, such that the methylation pattern in each single molecule tested is revealed.

As used herein, the term “defined set” of CpG loci (or other methylation loci) refers to the set of CpG loci in a marker gene or region selected for methylation analysis. A defined set of CpG loci in a marker gene or region may comprise all CpG loci in the gene or region, or it may comprise fewer than all of the loci in that gene or region.

As used herein the term “defined subset” of CpG loci (or other methylation loci) refers to a subset of the defined set of CpG loci in a marker gene or region, the methylation of which has been determined to be indicative of a non-normal state, e.g., adenoma or cancer. For example, in coordinate methylation analysis to determine the presence of colorectal cancer, the methylation status of a defined subset of CpG loci in at least one cancer marker nucleic acid molecule is determined, with simultaneous methylation at all of said CpG loci in the defined subset being indicative of cancer in the sample. A defined subset of CpG loci in a marker gene or region may comprise all CpG loci in the defined set, or it may comprise fewer than all of the loci in the defined set of loci in that gene or region.

As used herein, the term “colorectal cancer” is meant to include the well-accepted medical definition that defines colorectal cancer as a medical condition characterized by cancer of cells of the intestinal tract below the small intestine (e.g., the large intestine (colon), including the cecum, ascending colon, transverse colon, descending colon, and sigmoid colon, and rectum). Additionally, as used herein, the term “colorectal cancer” is meant to further include medical conditions which are characterized by cancer of cells of the duodenum and small intestine (jejunum and ileum).

As used herein, the term “metastasis” is meant to refer to the process in which cancer cells originating in one organ or part of the body relocate to another part of the body and continue to replicate. Metastasized cells subsequently form tumors which may further metastasize. Metastasis thus refers to the spread of cancer from the part of the body where it originally occurs to other parts of the body. As used herein, the term “metastasized colorectal cancer cells” is meant to refer to colorectal cancer cells which have metastasized; colorectal cancer cells localized in a part of the body other than the duodenum, small intestine (jejunum and ileum), large intestine (colon), including the cecum, ascending colon, transverse colon, descending colon, and sigmoid colon, and rectum.

As used herein, “an individual is suspected of being susceptible to metastasized colorectal cancer” is meant to refer to an individual who is at an above-average risk of developing metastasized colorectal cancer. Examples of individuals at a particular risk of developing metastasized colorectal cancer are those whose family medical history indicates above average incidence of colorectal cancer among family members and/or those who have already developed colorectal cancer and have been effectively treated who therefore face a risk of relapse and recurrence. Other factors which may contribute to an above-average risk of developing metastasized colorectal cancer which would thereby lead to the classification of an individual as being suspected of being susceptible to metastasized colorectal cancer may be based upon an individual's specific genetic, medical and/or behavioral background and characteristics.

The term “neoplasm” as used herein refers to any new and abnormal growth of tissue. Thus, a neoplasm can be a premalignant neoplasm or a malignant neoplasm.

The term “neoplasm-specific marker” refers to any biological material that can be used to indicate the presence of a neoplasm. Examples of biological materials include, without limitation, nucleic acids, polypeptides, carbohydrates, fatty acids, cellular components (e.g., cell membranes and mitochondria), and whole cells. In some instances, markers are particular nucleic acid regions, e.g., genes, intragenic regions, etc. Regions of nucleic acid that are markers may be referred to, e.g., as “marker genes,” “marker regions,” “marker sequences,” etc.

The term “colorectal neoplasm-specific marker” refers to any biological material that can be used to indicate the presence of a colorectal neoplasm (e.g., a premalignant colorectal neoplasm; a malignant colorectal neoplasm). Examples of colorectal neoplasm-specific markers include, but are not limited to, exfoliated epithelial markers (e.g., bmp-3, bmp-4, SFRP2, vimentin, septin9, ALX4, EYA4, TFPI2, NDRG4, FOXE1, long DNA, BAT-26, K-ras, APC, melanoma antigen gene, p53, BRAF, and PIK3CA) and fecal occult blood markers (e.g., hemoglobin, alpha-defensin, calprotectin, α1-antitrypsin, albumin, MCM2, transferrin, lactoferrin, and lysozyme). See also U.S. Pat. Nos. 7,485,420; 7,432,050; 5,352,775; 5,648,212; USRE36713; U.S. Pat. Nos. 5,527,676; 5,955,263; 6,090,566; 6,245,515; 6,677,312; 6,800,617; 7,087,583; and 7,267,955, each incorporated herein by reference.

Additional markers include but are not limited those in Table 1, below:

TABLE 1 Accession Symbol GeneID Reference NM_000038 APC 324 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_000044 AR 367 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf AB033043 KIAA1217 56243 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ AK055404 KIAA0984 23329 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ AK090480 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ BC041476 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ BX648962 DKFZp686K1684 440034 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000017 ACADS 35 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000022 ADA 100; www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ 79015 NM_000038 APC 324 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000038 APC 324 Weber et al. Nature Genetics 37(8), 2005, 853-862 NM_000043 FAS 355; Weber et al. Nature Genetics 37(8), 2005, 853-862 819114 NM_000044 AR 367 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000044 AR 367 Weber et al. Nature Genetics 37(8), 2005, 853-862 NM_000076 CDKN1C 1028 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000077 CDKN2A 1029; DNA Methylation And Cancer Therapy, Landes 51198 Bioscience 2005, ed. Moshe Szyf NM_000077 CDKN2A 1029; www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ 51198 NM_000077 CDKN2A 1029; Weber et al. Nature Genetics 37(8), 2005, 853-862 51198 NM_000088 COL1A1 1277 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000095 COMP 1311 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000104 CYP1B1 1545 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000115 EDNRB 1910 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000115 EDNRB 1910 Weber et al. Nature Genetics 37(8), 2005, 853-862 NM_000115 EDNRB 1910 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000125 ESR1 2099 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_000125 ESR1 2099 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_000125 ESR1 2099 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000125 ESR1 2099 Weber et al. Nature Genetics 37(8), 2005, 853-862 NM_000182 HADHA 3030 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000193 SHH 6469 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000249 MLH1 4292 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_000249 MLH1 4292 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000280 PAX6 5080 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000280 PAX6 5080 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000280 PAX6 5080 Weber et al. Nature Genetics 37(8), 2005, 853-862 NM_000308 PPGB 5476 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000314 PTEN 5728 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_000321 RB1 5925 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_000321 RB1 5925 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000336 SCNN1B 6338 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000362 TIMP3 7078 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_000362 TIMP3 7078 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000362 TIMP3 7078 Weber et al. Nature Genetics 37(8), 2005, 853-862 NM_000378 WT1 7490 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000402 G6PD 2539 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000438 PAX3 5077 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000443 ABCB4 5244 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000453 SLC5A5 6528 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000453 SLC5A5 6528 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000475 NR0B1 190 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000492 CFTR 1080 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000492 CFTR 1080 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000514 GDNF 2668 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000517 HBA2 3040 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000520 HEXA 3073; Keshet et al. Nature Genetics 38(2), 2006, 149-153 80072 NM_000524 HTR1A 3350 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000551 VHL 7428 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_000551 VHL 7428 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000551 VHL 7428 Weber et al. Nature Genetics 37(8), 2005, 853-862 NM_000610 CD44 960 Weber et al. Nature Genetics 37(8), 2005, 853-862 NM_000610 CD44 960 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000612 IGF2 3481; www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ 492304 NM_000620 NOS1 4842 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000680 ADRA1A 148 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000717 CA4 762 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000721 CACNA1E 777 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000782 CYP24A1 1591 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000799 EPO 2056 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000813 GABRB2 2561 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000818 GAD2 2572 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000829 GRIA4 2893 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000830 GRIK1 2897 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000834 GRIN2B 2904 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000843 GRM6 2916 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000852 GSTP1 2950 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_000852 GSTP1 2950 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000852 GSTP1 2950 Weber et al. Nature Genetics 37(8), 2005, 853-862 NM_000857 GUCY1B3 2983 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000863 HTR1B 3351 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000902 MME 4311 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000914 OPRM1 4988 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000915 OXT 5020 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000926 PGR 5241 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000927 ABCB1 5243 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000959 PTGFR 5737 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_000965 RARB 5915 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_000965 RARB 5915 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_000965 RARB 5915 Weber et al. Nature Genetics 37(8), 2005, 853-862 NM_000997 RPL37 6167 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001001336 CYB5R2 51700 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001001723 TMEM1 7109 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001002295 GATA3 2625 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001003689 L3MBTL2 83746 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001003891 PCQAP 51586 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_001007792 NTRK1 4914 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001008503 OPRM1 4988 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001008504 OPRM1 4988 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001008505 OPRM1 4988 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001009598 RXRG 6258 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001011545 BACH1 571 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001012331 NTRK1 4914 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001013464 LOC401363 401363; www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ 441242; 402532 NM_001018084 SLC26A10 65012 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001020658 PUM1 9698; Keshet et al. Nature Genetics 38(2), 2006, 149-153 28997 NM_001024844 CD82 3732 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_001025205 AP2M1 1173 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001025604 ARRDC2 27106 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001033044 GLUL 2752 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001033056 GLUL 2752 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001033518 WIPI2 26100 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001033519 WIPI2 26100 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001033520 WIPI2 26100 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001033952 CALCA 796 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_001036 RYR3 6263 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001043 SLC6A2 6530 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001053 SSTR5 6755 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001059 TACR3 6870 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001063 TF 7018 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001100 ACTA1 58 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001109 ADAM8 101 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001176 ARHGDIG 398 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001186 BACH1 571 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001204 BMPR2 659 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001228 CASP8 841 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_001250 CD40 958 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001257 CDH13 1012 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_001319 CSNK1G2 1455 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001325 CSTF2 1478 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001385 DPYS 1807; Keshet et al. Nature Genetics 38(2), 2006, 149-153 55412 NM_001451 FOXF1 2294 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001454 FOXJ1 2302 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001458 FLNC 2318 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001480 GALR1 2587 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001538 HSF4 3299 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001553 IGFBP7 3490; www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ 818325 NM_001572 IRF7 3665 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_001628 AKR1B1 231 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001635 AMPH 273 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001651 AQP5 362 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001718 BMP6 654 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_001753 CAV1 857 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_001753 CAV1 857 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_001768 CD8A 925 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001801 CDO1 1036 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001851 COL9A1 1297 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001883 CRHR2 1395 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001884 HAPLN1 1404 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001927 DES 1674 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001954 DDR1 780 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_001958 EEF1A2 1917 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001972 ELA2 1991 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001975 ENO2 2026 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_001989 EVX1 2128 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002007 FGF4 2249 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002012 FHIT 2272; www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ 246734 NM_002024 FMR1 2332 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_002065 GLUL 2752 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002110 HCK 3055 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002127 HLA-G 3135 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002148 HOXD10 3236 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002155 HSPA6 3310 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002191 INHA 3623 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_002212 ITGB4BP 3692 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002221 ITPKB 3707 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002235 KCNA6 3742 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002253 KDR 3791 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002344 LTK 4058 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002412 MGMT 4255 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_002457 MUC2 4583 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_002478 MYOD1 4654 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_002529 NTRK1 4914 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002588 PCDHGC3 5098; Keshet et al. Nature Genetics 38(2), 2006, 149-153 26025; 56108; 56112; 9708; 56109 NM_002658 PLAU 5328; www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ 414236 NM_002700 POU4F3 5459 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002807 PSMD1 5707; Keshet et al. Nature Genetics 38(2), 2006, 149-153 7410 NM_002848 PTPRO 5800 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002873 RAD17 5884 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_002923 RGS2 5997 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003027 SH3GL3 6457 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003088 FSCN1 6624 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003097 SNRPN 6638; www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ 8926; 145624; 8123; 63968; 3653 NM_003149 STAC 6769 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003204 NFE2L1 4779 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003219 TERT 7015 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_003238 TGFB2 7042 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003238 TGFB2 7042 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003246 THBS1 7057 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_003274 TMEM1 7109 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003277 CLDN5 7122 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003295 TPT1 7178; Keshet et al. Nature Genetics 38(2), 2006, 149-153 51447; 2982 NM_003300 TRAF3 7187 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003391 WNT2 7472 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003392 WNT5A 7474 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003408 ZFP37 7539; Keshet et al. Nature Genetics 38(2), 2006, 149-153 7551 NM_003417 ZNF264 9422 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003426 ZNF74 7625 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003435 ZNF134 7693 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003451 ZNF177 7730 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003474 ADAM12 8038 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003508 FZD9 8326 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003539 HIST1H4D 8360 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003540 HIST1H4F 8361 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003541 HIST1H4K 8362 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003546 HIST1H4L 8368 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003666 BLZF1 8548 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003735 PCDHGC3 5098; Keshet et al. Nature Genetics 38(2), 2006, 149-153 26025; 56108; 56112; 9708; 56109 NM_003736 PCDHGC3 5098; Keshet et al. Nature Genetics 38(2), 2006, 149-153 26025; 56108; 56112; 9708; 56109 NM_003775 EDG6 8698 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003777 DNAH11 8701; Keshet et al. Nature Genetics 38(2), 2006, 149-153 9026 NM_003806 HRK 8739 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_003823 TNFRSF6B 8771; Keshet et al. Nature Genetics 38(2), 2006, 149-153 51750; 10139 NM_003888 ALDH1A2 8854 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003914 CCNA1 8900 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_003923 FOXH1 8928 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003984 SLC13A2 9058 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_003991 EDNRB 1910 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_003999 OSMR 9180 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004004 GJB2 2706 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_004064 CDKN1B 1027 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_004068 AP2M1 1173 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004102 FABP3 2170 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_004113 FGF12 2257 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004122 GHSR 2693 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004135 IDH3G 3421 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004181 UCHL1 7345 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004230 EDG5 9294 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004248 PRLHR 2834 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004267 CHST2 9435 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004291 CART 9607 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004297 GNA14 9630 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004327 BCR 613; www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ 26226 NM_004360 CDH1 999 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_004360 CDH1 999 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_004360 CDH1 999 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_004378 CRABP1 1381 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004385 CSPG2 1462 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_004387 NKX2-5 1482 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_004394 DAP 1611 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004411 DYNC1I1 1780 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004441 EPHB1 2047 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004464 FGF5 2250 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004477 FRG1 2483 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004480 FUT8 2530 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004484 GPC3 2719 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_004525 LRP2 4036 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_004530 MMP2 4313 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004612 TGFBR1 7046 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_004621 TRPC6 7225 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004714 DYRK1B 9149 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004737 LARGE 9215 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004787 SLIT2 9353 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004817 TJP2 9414 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_004865 TBPL1 9519 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004887 CXCL14 9547 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004929 CALB1 793 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_004936 CDKN2B 1030 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_004936 CDKN2B 1030 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_004938 DAPK1 1612 DNA Methylation And Cancer Therapy, Landes Bioscience 2005, ed. Moshe Szyf NM_004975 KCNB1 3745 Keshet et al. 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Nature Genetics 38(2), 2006, 149-153 NM_024826 ASAP 79884 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_024882 C6orf155 79940 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_024893 C20orf39 79953 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_024944 CHODL 140578 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_025019 TUBA4 80086 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_025058 TRIM46 80128 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_025061 LRRC8E 80131 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_025087 FLJ21511 80157 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_025197 CDK5RAP3 80279 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_025204 RP3-402G11.12 80305 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_025208 PDGFD 80310; Keshet et al. Nature Genetics 38(2), 2006, 149-153 414301 NM_025218 ULBP1 80329 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_025263 PRR3 80742 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_030577 MGC10993 80775 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_030667 PTPRO 5800 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_030668 PTPRO 5800 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_030669 PTPRO 5800 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_030670 PTPRO 5800 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_030671 PTPRO 5800 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_030760 EDG8 53637 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_030806 C1orf21 81563; Keshet et al. Nature Genetics 38(2), 2006, 149-153 116492 NM_030920 ANP32E 81611 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_031277 RNF17 56163 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_031283 TCF7L1 83439 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_031424 C20orf55 83541 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_031466 NIBP 83696 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_031488 L3MBTL2 83746 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_031497 PCDHA6 56142; Keshet et al. Nature Genetics 38(2), 2006, 149-153 56145; 56134; 56147; 56146; 56139 NM_031856 PCDHA6 56142; Keshet et al. Nature Genetics 38(2), 2006, 149-153 56145; 56134; 56147; 56146; 56139 NM_031859 PCDHA6 56142; Keshet et al. Nature Genetics 38(2), 2006, 149-153 56145; 56134; 56147; 56146; 56139 NM_031860 PCDHA6 56142; Keshet et al. Nature Genetics 38(2), 2006, 149-153 56145; 56134; 56147; 56146; 56139 NM_031882 PCDHA6 56142; Keshet et al. Nature Genetics 38(2), 2006, 149-153 56145; 56134; 56147; 56146; 56139 NM_031883 PCDHA6 56142; Keshet et al. Nature Genetics 38(2), 2006, 149-153 56145; 56134; 56147; 56146; 56139 NM_031901 MRPS21 54460 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_031912 SYT15 83849 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_031922 REPS1 85021 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_031934 RAB34 83871 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_031994 RNF17 56163 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032034 SLC4A11 83959 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032087 PCDHGC3 5098; Keshet et al. Nature Genetics 38(2), 2006, 149-153 26025; 56108; 56112; 9708; 56109 NM_032094 PCDHGC3 5098; Keshet et al. Nature Genetics 38(2), 2006, 149-153 26025; 56108; 56112; 9708; 56109 NM_032098 PCDHGC3 5098; Keshet et al. Nature Genetics 38(2), 2006, 149-153 26025; 56108; 56112; 9708; 56109 NM_032099 PCDHGC3 5098; Keshet et al. Nature Genetics 38(2), 2006, 149-153 26025; 56108; 56112; 9708; 56109 NM_032100 PCDHGC3 5098; Keshet et al. Nature Genetics 38(2), 2006, 149-153 26025; 56108; 56112; 9708; 56109 NM_032101 PCDHGC3 5098; Keshet et al. Nature Genetics 38(2), 2006, 149-153 26025; 56108; 56112; 9708; 56109 NM_032109 OTP 23440 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_032134 QRICH2 84074 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032140 C16orf48 84080 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032192 PPP1R1B 84152 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032256 TMEM117 84216 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032303 HSDL2 84263 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032391 PRAC 84366 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032402 PCDHGC3 5098; Keshet et al. Nature Genetics 38(2), 2006, 149-153 26025; 56108; 56112; 9708; 56109 NM_032403 PCDHGC3 5098; Keshet et al. Nature Genetics 38(2), 2006, 149-153 26025; 56108; 56112; 9708; 56109 NM_032406 PCDHGC3 5098; Keshet et al. Nature Genetics 38(2), 2006, 149-153 26025; 56108; 56112; 9708; 56109 NM_032411 ECRG4 84417 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032412 ORF1-FL49 84418 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032603 LOXL3 84695 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032625 C7orf13 129790 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032803 SLC7A3 84889 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032825 ZNF382 84911 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032838 ZNF566 84924 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032883 C20orf100 84969 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032918 RERG 85004 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032945 TNFRSF6B 8771; Keshet et al. Nature Genetics 38(2), 2006, 149-153 51750; 10139 NM_032961 PCDH10 57575 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_032967 PCDH11X 27328; Keshet et al. Nature Genetics 38(2), 2006, 149-153 83259 NM_032968 PCDH11X 27328; Keshet et al. Nature Genetics 38(2), 2006, 149-153 83259 NM_032969 PCDH11X 27328; Keshet et al. Nature Genetics 38(2), 2006, 149-153 83259 NM_033126 PSKH2 85481 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_033135 PDGFD 80310; Keshet et al. Nature Genetics 38(2), 2006, 149-153 414301 NM_033143 FGF5 2250 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_033224 PURB 5814 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_033302 ADRA1A 148 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_033303 ADRA1A 148 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_033304 ADRA1A 148 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_033445 HIST3H2A 92815 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_033624 FBXO21 23014 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_052902 STK11IP 114790 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_052954 CYYR1 116159 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_052961 SLC26A8 116369 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_052978 TRIM9 114088 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_054021 GPR101 83550 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_054108 HRASLS5 117245 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_058165 MOGAT1 116255 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_078485 COL9A1 1297 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_080552 SLC32A1 140679 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_080617 CBLN4 140689 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_080671 KCNE4 23704 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_080742 B3GAT2 135152 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_130773 CNTNAP5 129684 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_130900 RAET1L 154064 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_133180 EPS8L1 54869 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_133266 SHANK2 22941 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_133338 RAD17 5884 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_133339 RAD17 5884 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_133340 RAD17 5884 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_133341 RAD17 5884 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_133342 RAD17 5884 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_133343 RAD17 5884 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_133344 RAD17 5884 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_133489 SLC26A10 65012 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_133493 CD109 135228 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_133642 LARGE 9215 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_138290 RPIB9 154661 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_138718 SLC26A8 116369 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_138996 CNTNAP5 129684 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_139204 EPS8L1 54869 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_139316 AMPH 273 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_144497 AKAP12 9590 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_144725 FLJ25439 153657 Weber et al. Nature Genetics 37(8), 2005, 853-862 NM_145725 TRAF3 7187 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_145726 TRAF3 7187 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_152562 CDCA2 157313 Weber et al. Nature Genetics 37(8), 2005, 853-862 NM_152854 CD40 958 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_153819 RASGRP2 10235 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_170696 ALDH1A2 8854 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_170697 ALDH1A2 8854 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_170775 KCNN2 3781 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_171827 CD8A 925 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_172337 OTX2 5015 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_173479 LOC126248 126248 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_174869 IDH3G 3421 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_175052 ST8SIA4 7903 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_175611 GRIK1 2897 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_175709 CBX7 23492 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_175768 GRIK2 2898 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_176095 CDK5RAP3 80279 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_176096 CDK5RAP3 80279 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_177555 TRO 7216 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_177556 TRO 7216 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_177557 TRO 7216 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_177558 TRO 7216 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_177959 DOK5 55816 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_178154 FUT8 2530 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_178155 FUT8 2530 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_178156 FUT8 2530 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_178157 FUT8 2530 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_181457 PAX3 5077 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_181458 PAX3 5077 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_181459 PAX3 5077 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_181460 PAX3 5077 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_181461 PAX3 5077 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_181466 ITGB4BP 3692 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_181467 ITGB4BP 3692 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_181468 ITGB4BP 3692 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_181469 ITGB4BP 3692 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_181505 PPP1R1B 84152 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_181657 LTB4R 1241 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_181689 NNAT 4826 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_182609 ZNF677 342926 Weber et al. Nature Genetics 37(8), 2005, 853-862 NM_198265 SPO11 23626 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_198287 ING4 51147 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_198407 GHSR 2693 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_198570 UNQ739 375567 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_198849 LOC283514 283514 Weber et al. Nature Genetics 37(8), 2005, 853-862 NM_199051 FAM5C 339479 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_199076 CNNM2 54805 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_199077 CNNM2 54805 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_199231 GDNF 2668 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_199234 GDNF 2668 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_199425 VSX1 30813 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_199426 ZFP64 55734 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_199427 ZFP64 55734 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NM_199427 ZFP64 55734 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_201647 STAMBP 10617 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_201999 ELF2 1998; Keshet et al. Nature Genetics 38(2), 2006, 149-153 26472 NM_206827 RASL11A 387496 Weber et al. Nature Genetics 37(8), 2005, 853-862 NM_206866 BACH1 571 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_206961 LTK 4058 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_207121 C20orf55 83541 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NM_213622 STAMBP 10617 Keshet et al. Nature Genetics 38(2), 2006, 149-153 NP_536846 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/ NR_002196 www(dot) methdb(dot)de/ and/or mdanderson(dot)org/

See also Ilana Keshet, et al., Nature Genetics 38, 149-153 (1 Feb. 2006) and Gerd P Pfeifer, et al., Expert Opinion on Medical Diagnostics, September 2007, Vol. 1, No. 1, Pages 99-108, each of which is incorporated herein by reference.

As used herein, the term “adenoma” refers to a benign tumor of glandular origin. Although these growths are benign, over time they may progress to become malignant. As used herein the term “colorectal adenoma” refers to a benign colorectal tumor in which the cells form recognizable glandular structures or in which the cells are clearly derived from glandular epithelium.

The term “amplifying” or “amplification” in the context of nucleic acids refers to the production of multiple copies of a polynucleotide, or a portion of the polynucleotide, typically starting from a small amount of the polynucleotide (e.g., a single polynucleotide molecule), where the amplification products or amplicons are generally detectable. Amplification of polynucleotides encompasses a variety of chemical and enzymatic processes. The generation of multiple DNA copies from one or a few copies of a target or template DNA molecule during a polymerase chain reaction (PCR) or a ligase chain reaction (LCR; see, e.g., U.S. Pat. No. 5,494,810; herein incorporated by reference in its entirety) are forms of amplification. Additional types of amplification include, but are not limited to, allele-specific PCR (see, e.g., U.S. Pat. No. 5,639,611; herein incorporated by reference in its entirety), assembly PCR (see, e.g., U.S. Pat. No. 5,965,408; herein incorporated by reference in its entirety), helicase-dependent amplification (see, e.g., U.S. Pat. No. 7,662,594; herein incorporated by reference in its entirety), Hot-start PCR (see, e.g., U.S. Pat. Nos. 5,773,258 and 5,338,671; each herein incorporated by reference in their entireties), intersequence-specfic PCR, inverse PCR (see, e.g., Triglia, et al. (1988) Nucleic Acids Res., 16:8186; herein incorporated by reference in its entirety), ligation-mediated PCR (see, e.g., Guilfoyle, R. et al., Nucleic Acids Research, 25:1854-1858 (1997); U.S. Pat. No. 5,508,169; each of which are herein incorporated by reference in their entireties), methylation-specific PCR (see, e.g., Herman, et al., (1996) PNAS 93(13) 9821-9826; herein incorporated by reference in its entirety), miniprimer PCR, multiplex ligation-dependent probe amplification (see, e.g., Schouten, et al., (2002) Nucleic Acids Research 30(12): e57; herein incorporated by reference in its entirety), multiplex PCR (see, e.g., Chamberlain, et al., (1988) Nucleic Acids Research 16(23) 11141-11156; Ballabio, et al., (1990) Human Genetics 84(6) 571-573; Hayden, et al., (2008) BMC Genetics 9:80; each of which are herein incorporated by reference in their entireties), nested PCR, overlap-extension PCR (see, e.g., Higuchi, et al., (1988) Nucleic Acids Research 16(15) 7351-7367; herein incorporated by reference in its entirety), real time PCR (see, e.g., Higuchi, et al., (1992) Biotechnology 10:413-417; Higuchi, et al., (1993) Biotechnology 11:1026-1030; each of which are herein incorporated by reference in their entireties), reverse transcription PCR (see, e.g., Bustin, S. A. (2000) J. Molecular Endocrinology 25:169-193; herein incorporated by reference in its entirety), solid phase PCR, thermal asymmetric interlaced PCR, and Touchdown PCR (see, e.g., Don, et al., Nucleic Acids Research (1991) 19(14) 4008; Roux, K. (1994) Biotechniques 16(5) 812-814; Hecker, et al., (1996) Biotechniques 20(3) 478-485; each of which are herein incorporated by reference in their entireties). Polynucleotide amplification also can be accomplished using digital PCR (see, e.g., Kalinina, et al., Nucleic Acids Research. 25; 1999-2004, (1997); Vogelstein and Kinzler, Proc Natl Acad Sci USA. 96; 9236-41, (1999); International Patent Publication No. WO05023091A2; US Patent Application Publication No. 20070202525; each of which are incorporated herein by reference in their entireties).

The term “polymerase chain reaction” (“PCR”) refers to the method of K. B. Mullis U.S. Pat. Nos. 4,683,195, 4,683,202, and 4,965,188, that describe a method for increasing the concentration of a segment of a target sequence in a mixture of genomic DNA without cloning or purification. This process for amplifying the target sequence consists of introducing a large excess of two oligonucleotide primers to the DNA mixture containing the desired target sequence, followed by a precise sequence of thermal cycling in the presence of a DNA polymerase. The two primers are complementary to their respective strands of the double stranded target sequence. To effect amplification, the mixture is denatured and the primers then annealed to their complementary sequences within the target molecule. Following annealing, the primers are extended with a polymerase so as to form a new pair of complementary strands. The steps of denaturation, primer annealing, and polymerase extension can be repeated many times (i.e., denaturation, annealing and extension constitute one “cycle”; there can be numerous “cycles”) to obtain a high concentration of an amplified segment of the desired target sequence. The length of the amplified segment of the desired target sequence is determined by the relative positions of the primers with respect to each other, and therefore, this length is a controllable parameter. By virtue of the repeating aspect of the process, the method is referred to as the “polymerase chain reaction” (“PCR”). Because the desired amplified segments of the target sequence become the predominant sequences (in terms of concentration) in the mixture, they are said to be “PCR amplified” and are “PCR products” or “amplicons.”

As used herein, the term “nucleic acid detection assay” refers to any method of determining the nucleotide composition of a nucleic acid of interest. Nucleic acid detection assay include but are not limited to, DNA sequencing methods, probe hybridization methods, structure specific cleavage assays (e.g., the INVADER assay, (Hologic, Inc.) and are described, e.g., in U.S. Pat. Nos. 5,846,717, 5,985,557, 5,994,069, 6,001,567, 6,090,543, and 6,872,816; Lyamichev et al., Nat. Biotech., 17:292 (1999), Hall et al., PNAS, USA, 97:8272 (2000), and US 2009/0253142, each of which is herein incorporated by reference in its entirety for all purposes); enzyme mismatch cleavage methods (e.g., Variagenics, U.S. Pat. Nos. 6,110,684, 5,958,692, 5,851,770, herein incorporated by reference in their entireties); polymerase chain reaction; branched hybridization methods (e.g., Chiron, U.S. Pat. Nos. 5,849,481, 5,710,264, 5,124,246, and 5,624,802, herein incorporated by reference in their entireties); rolling circle replication (e.g., U.S. Pat. Nos. 6,210,884, 6,183,960 and 6,235,502, herein incorporated by reference in their entireties); NASBA (e.g., U.S. Pat. No. 5,409,818, herein incorporated by reference in its entirety); molecular beacon technology (e.g., U.S. Pat. No. 6,150,097, herein incorporated by reference in its entirety); E-sensor technology (Motorola, U.S. Pat. Nos. 6,248,229, 6,221,583, 6,013,170, and 6,063,573, herein incorporated by reference in their entireties); cycling probe technology (e.g., U.S. Pat. Nos. 5,403,711, 5,011,769, and 5,660,988, herein incorporated by reference in their entireties); Dade Behring signal amplification methods (e.g., U.S. Pat. Nos. 6,121,001, 6,110,677, 5,914,230, 5,882,867, and 5,792,614, herein incorporated by reference in their entireties); ligase chain reaction (e.g., Barnay Proc. Natl. Acad. Sci USA 88, 189-93 (1991)); and sandwich hybridization methods (e.g., U.S. Pat. No. 5,288,609, herein incorporated by reference in its entirety).

As used herein, the terms “complementary” or “complementarity” used in reference to polynucleotides (i.e., a sequence of nucleotides) refers to polynucleotides related by the base-pairing rules. For example, the sequence “5′-A-G-T-3′,” is complementary to the sequence “3′-T-C-A-5′.” Complementarity may be “partial,” in which only some of the nucleic acids' bases are matched according to the base pairing rules. Or, there may be “complete” or “total” complementarity between the nucleic acids. The degree of complementarity between nucleic acid strands has significant effects on the efficiency and strength of hybridization between nucleic acid strands. This is of particular importance in amplification reactions, as well as detection methods that depend upon binding between nucleic acids.

As used herein, the term “primer” refers to an oligonucleotide, whether occurring naturally, as in a purified restriction digest, or produced synthetically, that is capable of acting as a point of initiation of synthesis when placed under conditions in which synthesis of a primer extension product that is complementary to a nucleic acid strand is induced (e.g., in the presence of nucleotides and an inducing agent such as a biocatalyst (e.g., a DNA polymerase or the like). The primer is typically single stranded for maximum efficiency in amplification, but may alternatively be partially or completely double stranded. The portion of the primer that hybridizes to a template nucleic acid is sufficiently long to prime the synthesis of extension products in the presence of the inducing agent. The exact lengths of the primers will depend on many factors, including temperature, source of primer and the use of the method. Primers may comprise labels, tags, capture moieties, etc.

As used herein, the term “nucleic acid molecule” refers to any nucleic acid containing molecule, including but not limited to, DNA or RNA. The term encompasses sequences that include any of the known base analogs of DNA and RNA including, but not limited to, 4 acetylcytosine, 8-hydroxy-N6-methyladenosine, aziridinylcytosine, pseudoisocytosine, 5-(carboxyhydroxyl-methyl) uracil, 5-fluorouracil, 5-bromouracil, 5-carboxymethylaminomethyl-2-thiouracil, 5-carboxymethyl-aminomethyluracil, dihydrouracil, inosine, N6-isopentenyladenine, 1-methyladenine, 1-methylpseudo-uracil, 1-methylguanine, 1-methylinosine, 2,2-dimethyl-guanine, 2-methyladenine, 2-methylguanine, 3-methyl-cytosine, 5-methylcytosine, N6-methyladenine, 7-methylguanine, 5-methylaminomethyluracil, 5-methoxy-amino-methyl-2-thiouracil, beta-D-mannosylqueosine, 5′-methoxycarbonylmethyluracil, 5-methoxyuracil, 2-methylthio-N-isopentenyladenine, uracil-5-oxyacetic acid methylester, uracil-5-oxyacetic acid, oxybutoxosine, pseudouracil, queosine, 2-thiocytosine, 5-methyl-2-thiouracil, 2-thiouracil, 4-thiouracil, 5-methyluracil, N-uracil-5-oxyacetic acid methylester, uracil-5-oxyacetic acid, pseudouracil, queosine, 2-thiocytosine, and 2,6-diaminopurine.

As used herein, the term “nucleobase” is synonymous with other terms in use in the art including “nucleotide,” “deoxynucleotide,” “nucleotide residue,” “deoxynucleotide residue,” “nucleotide triphosphate (NTP),” or deoxynucleotide triphosphate (dNTP).

An “oligonucleotide” refers to a nucleic acid that includes at least two nucleic acid monomer units (e.g., nucleotides), typically more than three monomer units, and more typically greater than ten monomer units. The exact size of an oligonucleotide generally depends on various factors, including the ultimate function or use of the oligonucleotide. To further illustrate, oligonucleotides are typically less than 200 residues long (e.g., between 15 and 100), however, as used herein, the term is also intended to encompass longer polynucleotide chains. Oligonucleotides are often referred to by their length. For example a 24 residue oligonucleotide is referred to as a “24-mer”. Typically, the nucleoside monomers are linked by phosphodiester bonds or analogs thereof, including phosphorothioate, phosphorodithioate, phosphoroselenoate, phosphorodiselenoate, phosphoroanilothioate, phosphoranilidate, phosphoramidate, and the like, including associated counterions, e.g., H⁺, NH₄ ⁺, Na⁺, and the like, if such counterions are present. Further, oligonucleotides are typically single-stranded. Oligonucleotides are optionally prepared by any suitable method, including, but not limited to, isolation of an existing or natural sequence, DNA replication or amplification, reverse transcription, cloning and restriction digestion of appropriate sequences, or direct chemical synthesis by a method such as the phosphotriester method of Narang et al. (1979) Meth Enzymol. 68: 90-99; the phosphodiester method of Brown et al. (1979) Meth Enzymol. 68: 109-151; the diethylphosphoramidite method of Beaucage et al. (1981) Tetrahedron Lett. 22: 1859-1862; the triester method of Matteucci et al. (1981) J Am Chem Soc. 103:3185-3191; automated synthesis methods; or the solid support method of U.S. Pat. No. 4,458,066, entitled “PROCESS FOR PREPARING POLYNUCLEOTIDES,” issued Jul. 3, 1984 to Caruthers et al., or other methods known to those skilled in the art. All of these references are incorporated by reference.

A “sequence” of a biopolymer refers to the order and identity of monomer units (e.g., nucleotides, amino acids, etc.) in the biopolymer. The sequence (e.g., base sequence) of a nucleic acid is typically read in the 5′ to 3′ direction.

The term “wild-type” refers to a gene or gene product that has the characteristics of that gene or gene product when isolated from a naturally occurring source. A wild-type gene is that which is most frequently observed in a population and is thus arbitrarily designed the “normal” or “wild-type” form of the gene. In contrast, the terms “modified,” “mutant,” and “variant” refer to a gene or gene product that displays modifications in sequence and or functional properties (i.e., altered characteristics) when compared to the wild-type gene or gene product. It is noted that naturally occurring mutants can be isolated; these are identified by the fact that they have altered characteristics when compared to the wild-type gene or gene product.

As used herein, the term “gene” refers to a nucleic acid (e.g., DNA) sequence that comprises coding sequences necessary for the production of a polypeptide, precursor, or RNA (e.g., rRNA, tRNA). The polypeptide can be encoded by a full length coding sequence or by any portion of the coding sequence so long as the desired activity or functional properties (e.g., enzymatic activity, ligand binding, signal transduction, immunogenicity, etc.) of the full-length or fragment polypeptide are retained. The term also encompasses the coding region of a structural gene and the sequences located adjacent to the coding region on both the 5′ and 3′ ends for a distance of about 1 kb or more on either end such that the gene corresponds to the length of the full-length mRNA. Sequences located 5′ of the coding region and present on the mRNA are referred to as 5′ non-translated sequences. Sequences located 3′ or downstream of the coding region and present on the mRNA are referred to as 3′ non-translated sequences. The term “gene” encompasses both cDNA and genomic forms of a gene. A genomic form or clone of a gene contains the coding region interrupted with non-coding sequences termed “introns” or “intervening regions” or “intervening sequences.” Introns are segments of a gene that are transcribed into nuclear RNA (e.g., hnRNA); introns may contain regulatory elements (e.g., enhancers). Introns are removed or “spliced out” from the nuclear or primary transcript; introns therefore are absent in the messenger RNA (mRNA) transcript. The mRNA functions during translation to specify the sequence or order of amino acids in a nascent polypeptide.

In addition to containing introns, genomic forms of a gene may also include sequences located on both the 5′ and 3′ end of the sequences that are present on the RNA transcript. These sequences are referred to as “flanking” sequences or regions (these flanking sequences are located 5′ or 3′ to the non-translated sequences present on the mRNA transcript). The 5′ flanking region may contain regulatory sequences such as promoters and enhancers that control or influence the transcription of the gene. The 3′ flanking region may contain sequences that direct the termination of transcription, post-transcriptional cleavage and polyadenylation.

As used herein, the terms “multimethylation,” “series methylation” and “specific methylation” are used interchangeably to refer to defined combinations of CpG sites or loci in a marker sequence must be methylated to call that sequence methylated in a coordinate or multimethylation assay. For example, a specific methylation assay of the CpG sites for BMP3 might require that the CpG positions at 23, 34, 53, 61, 70, and 74, numbered by reference to FIGS. 1A and 1B, all be methylated in order for a sample to be classified as methylated at the BMP3 marker. Specific methylation of BMP3 is not limited to this set of particular loci, but may include more, fewer, or a different collection of CpG loci. The CpG loci selected for co-analysis in a multimethylation assay are preferably selected. e.g., by analysis of normal (non-adenoma, non-cancer) samples to identify CpG methylation combinations that are less frequently represented in normal samples. In preferred embodiments, combinations of methylation sites are selected to produce good signal-to-noise in cancer and adenoma samples (i.e., the mean multimethylation at a particular combination of loci in cancer samples divided by the mean multimethylation in at those loci in normal samples is high).

As used herein, the terms “individual” and “average” methylation are used interchangeably to refer to analyses in which each CpG locus is analyzed individually, such that all molecules in which that base is methylated are included in a count, regardless of the methylation status of other loci, e.g., in the same marker. Generally, the methylation percentages of all the loci in a marker/region are then averaged, to produce a percent methylation figure for that marker.

As used herein, the term “kit” refers to any delivery system for delivering materials. In the context of reaction assays, such delivery systems include systems that allow for the storage, transport, or delivery of reaction reagents (e.g., oligonucleotides, enzymes, etc. in the appropriate containers) and/or supporting materials (e.g., buffers, written instructions for performing the assay etc.) from one location to another. For example, kits include one or more enclosures (e.g., boxes) containing the relevant reaction reagents and/or supporting materials. As used herein, the term “fragmented kit” refers to a delivery systems comprising two or more separate containers that each contain a subportion of the total kit components. The containers may be delivered to the intended recipient together or separately. For example, a first container may contain an enzyme for use in an assay, while a second container contains oligonucleotides. The term “fragmented kit” is intended to encompass kits containing Analyte specific reagents (ASR's) regulated under section 520(e) of the Federal Food, Drug, and Cosmetic Act, but are not limited thereto. Indeed, any delivery system comprising two or more separate containers that each contains a subportion of the total kit components are included in the term “fragmented kit.” In contrast, a “combined kit” refers to a delivery system containing all of the components of a reaction assay in a single container (e.g., in a single box housing each of the desired components). The term “kit” includes both fragmented and combined kits.

As used herein, the term “information” refers to any collection of facts or data. In reference to information stored or processed using a computer system(s), including but not limited to internets, the term refers to any data stored in any format (e.g., analog, digital, optical, etc.). As used herein, the term “information related to a subject” refers to facts or data pertaining to a subject (e.g., a human, plant, or animal). The term “genomic information” refers to information pertaining to a genome including, but not limited to, nucleic acid sequences, genes, allele frequencies, RNA expression levels, protein expression, phenotypes correlating to genotypes, etc. “Allele frequency information” refers to facts or data pertaining to allele frequencies, including, but not limited to, allele identities, statistical correlations between the presence of an allele and a characteristic of a subject (e.g., a human subject), the presence or absence of an allele in an individual or population, the percentage likelihood of an allele being present in an individual having one or more particular characteristics, etc.

DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawings will be provided by the Office upon request and payment of the necessary fee.

FIGS. 1A and 1B provide sequence and CpG information for exemplary marker regions used in the present analysis. For each target gene, the native sequence of the region is shown in the top line. Unmethylated C-residues that would be converted by bisulfite and amplification to Ts are shown as T residues. Candidate methylation positions are shown boxed. Reference numbering for base and CpG positions is shown above each native sequence. Primer locations for amplification are shown as a row of underlined base positions.

FIGS. 2 A-I provide tables showing the analyses of normal, adenoma, and cancer samples in which average methylation across all of the CpG loci indicated in Table 2 were calculated for each marker in each sample. For the normal samples in FIG. 2A, the average, standard deviation and the mean plus 2 or 3 standard deviations for each marker are indicated. For the adenoma and cancer samples, shaded cells in FIGS. 2B and 2C indicate a positive result, reflected as an average methylation value for that marker that is greater than the mean methylation+3 standard deviations determined for that marker in the normal samples.

FIGS. 2D and 2E show the calculated effect of a 20-fold dilution of adenoma and cancer DNA into normal DNA, FIGS. 2F and 2G show a calculated 10-fold dilution, and FIGS. 2H and 2I show a calculated 5-fold dilution. In each of the calculated dilutions, the average methylation for a marker is divided by the 20, 10, or 5, added to the mean methylation of the normal DNA for that marker. Shaded cells in FIGS. 2D-2I indicate an average methylation value for that marker that is greater than the mean methylation+2 standard deviations (specificity of 97.5%) determined for that marker in the normal samples.

Below each of FIGS. 2B-2I, the percentage of positive values for each marker in the sample type and dilution for that panel are indicated. The percentage of samples giving a positive signal for at least one of the Vimentin, BMP3, Septin 9 and TFPI2 markers are indicated at the bottom of each panel.

FIGS. 3A and 3B provide sequence and CpG information for exemplary genes used in the present analysis. The CpG loci in each marker gene included in the defined subsets of CpG loci for coordinate methylation analysis in colorectal adenoma and cancer samples are shown with a black background and in white typeface.

FIGS. 4 A-I provide tables showing the analyses of normal, adenoma, and cancer samples in which methylation was determined at each of the indicated CpG positions in the indicated marker regions (i.e., samples were assayed for the percentage of DNA copies that displayed methylation at all of the CpG loci in the defined subset). Each marker was tested at each of the CpG loci in the defined subsets indicated in FIGS. 3A and 3B and the percentage methylation data reflects the percentage of marker copies having methylation at all of the tested CpG loci (coordinate methylation or “multimethylation” analysis). For the normal samples in FIG. 4A, the mean multimethylation, standard deviation and the mean plus 2 or 3 standard deviations for each marker are indicated. For the adenoma and cancer samples, shaded cells in FIGS. 4B and 4C indicate a positive result, reflected as multimethylation value for that marker that is greater than the mean multimethylation+3 standard deviations determined for that marker in the normal samples.

FIGS. 4D and 4E show the calculated effect of a 20-fold dilution of adenoma and cancer DNA into normal DNA, FIGS. 4F and 4G show a calculated 10-fold dilution, and 4H and 41 show a calculated 5-fold dilution. In each of the calculated dilutions, the average multimethylation for a marker is divide by the 20, 10, or 5, added to the mean multimethylation of the normal DNA for that marker. Shaded cells in FIGS. 4D-4I indicate an average multimethylation value for that marker that is greater than the mean multimethylation+2 standard deviations (specificity of 97.5%) determined for that marker in the normal samples.

Below each of FIGS. 4B-4I, the percentage of positive values for each marker in the sample type and dilution for that panel are indicated. The percentage of samples giving a positive signal for at least one of the vimentin, BMP3, Septin 9 and TFPI2 markers are indicated at the bottom of each panel.

FIG. 5 shows a table and graph comparing the percent positive values calculated for each marker in adenoma and cancer samples, as indicated, using either individual/average methylation or multimethylation analysis methods to test each of the indicated markers, at each of the indicated calculated dilutions.

FIG. 6 shows a table and graph comparing the percent positive values determined in adenoma and cancer samples, determined using the four markers with the lowest mean background in these samples (vimentin, BMP3, Septin9, TFPI2), using either the individual/average methylation or the multimethylation analysis method, at each of the indicated calculated dilutions into normal DNA.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the invention are described in this summary, and in the Summary of the Invention, above, which is incorporated here by reference. Although the invention has been described in connection with specific embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments.

The present invention relates to methods and compositions for determination of, and uses of, specific methylation patterns indicative of adenoma and carcinoma. In particular, the invention relates to analysis of defined subsets of CpG loci that are coordinately methylated in DNAs from cancer and adenoma samples, methods for identifying coordinately methylated loci, and methods of using analysis of coordinately methylated loci in one or more markers or regions in the design of assays for adenoma and cancer having improved sensitivity and specificity.

The present invention relates to the observation that, within marker nucleic acids for which methylation status is indicative of cellular status, e.g., cancerous, pre-cancerous, normal, etc., a subset of the individual methylation loci, e.g., CpG loci, in non-normal cells generally displays a greater degree of methylation relative to the background levels of methylation observed at corresponding loci in normal cells, while other methylation loci in the non-normal cells may exhibit levels of methylation that are closer to background levels. In some embodiments, the degree of methylation observed for a particular locus in plurality of cancerous or pre-cancerous cells relative to normal cells is expressed as a methylation ratio.

Some embodiments of the present invention relate to screening known or suspected marker genes to identify specific methylation loci that exhibit greater ratios of disease-associated methylation relative to background methylation, as compared to other marker genes or other loci in the same marker gene. In some preferred embodiments, the present invention relates to coordinate methylation analysis, to measure the degree to which a marker molecule or a sample exhibits methylation at all of a plurality of selected loci.

The present invention relates to analyzing methylation statuses of a defined set of individual CpG loci in methylation markers (or target regions within such markers) in a significant enough number of individual DNA molecules in adenoma samples or cancer samples to identify defined subsets of CpG loci that have advantageous methylation ratios compared to other loci in the same adenoma or cancer samples. A defined subset of CpG loci that have advantageous methylation ratios in a sample may comprise the entirety of a set of CpG loci in a particular marker or target region of a marker, or it may be fewer than all of the CpG loci in the characterized region of the marker.

Conventional methods of analyzing methylation status of a marker generally involve analysis of a mixed population of molecules. For example, amplification of a marker nucleic acid from a sample generally produces a mixture of amplicons coming from many copies of a target molecule. If the amplification conditions are not selective for a gene variant, the amplicon product contains a mixture of the variant and the normal or wildtype DNA. Even if primers are specific for a mutation or for a particular methylation site, when DNA is amplified from many copies of target DNA derived from many cells, there can be heterogeneity in other base positions in the resulting amplicon. If these mixed amplicons are sequenced directly, the resulting sequence traces reveal the consensus sequence of the mixed population, and particular sequences or mutations present in a small portion of the population are essentially undetectable. Although some researchers have sequenced individual clones from such amplifications to examine sequence information for individual molecules from the mixture, only small numbers of molecules have been analyzed and the data gathered did not suggest any that any specific loci within the markers predictably exhibited advantageous methylation ratios compared to other methylated loci within the same target, or that coordinated analysis of loci having advantageous methylation ratios could be useful in improving the specificity and sensitivity of assays in detection of neoplasms. An aspect of the present invention is based on the observation that collecting methylation ratio information from a very large number of individual molecules in both normal and non-normal samples reveals that some methylation loci in marker regions or sequences exhibit a greater degree of methylation in non-normal cells compared to background (methylation at the same loci in normal cells) than do other individual loci in the same marker region or gene. These loci in non-normal sequences that have a greater level of methylation compared to background can be viewed as being particularly advantageous in that they are easier to identify over the background level of methylation observed in normal cells. One aspect of this advantage is that analysis of these particular loci permits identification of cancer-associated methylation with more sensitivity, and in a greater background of normal cells.

The present invention also relates to the observation that coordinated analysis of multiple loci provides a significantly enhanced level of sensitivity in the identification of cancerous or precancerous cells, especially in samples that may also comprise a significant number of normal cells. For example, FIG. 5 compares the sensitivity of detecting adenoma and cancer cells. For each of the indicated marker genes, the methylation was either determined as an average across the marker region (e.g., the mean methylation in the vimentin marker across all of loci 26, 37, 40, 45, 52, 54, 59, 63, and 74; see FIGS. 2A-I), indicated as “Individual” average methylation, or as a percentage of molecules displaying methylation at all of a subset of selected loci (e.g., methylation in the vimentin marker at all three of loci 37, 40 and 45; see FIGS. 4A-I), i.e., coordinate methylation analysis of multiple individual loci, indicated as “multi”. The sensitivities for the same samples are also shown in calculated 5, 10 or 20-fold dilutions into normal DNA. FIG. 5 shows that, while assay sensitivities may be similar in DNA analyzed directly from tissue without dilution, as the amount background from normal DNA is increased at the larger dilutions, the coordinate methylation analysis is shown to be far more sensitive than the average methylation analysis. For example, in the analysis of Septin9, the adenoma and cancer samples can be detected above background only in the undiluted and 5-fold dilution profiles when average methylation across the marker is analyzed, while these same samples can be detected with over about 69-74% sensitivity at 20-fold dilution, and 90-93% sensitivity at 10-fold dilution, when coordinate methylation analysis of loci 37, 40 and 45 is used.

In some embodiments, the present invention provides a method for designing a methylation assay to identify a disease state, comprising I) selecting at least one sequence for analysis; II) determining the methylation status of a plurality of loci in the sequence in a population of normal cells and a population of non-normal cells to determine an average rate of methylation for each of the plurality of loci each both normal and non-normal cells; and III) identifying at least two loci in said plurality of loci having advantageous methylation ratios.

I. Selection of sequence(s). Methylation markers associated with particular disease states have been identified for a number of disease states. For example, colorectal neoplasm-specific marker include, e.g., bmp-3, bmp-4, SFRP2, vimentin, septin9, ALX4, EYA4, TFPI2, NDRG4, FOXE1, long DNA, BAT-26, K-ras, APC, melanoma antigen gene, p53, BRAF, and PIK3CA. Additional markers include but are not limited those in Table 1, above. Analysis of candidate methylation loci to identify those with advantageous methylation ratios may comprise analysis of every locus in a target sequence (e.g., every CpG) or it may comprise analysis of a subset of the methylation loci. In some embodiments, CpGs are selected for analysis by their location in particular methylation hotspots, while in other embodiments, loci for analysis may be conveniently located with respect to primer binding sites or other sequence features. FIG. 1A provides an exemplary selection of marker neoplasm-associated markers with each of the C residues in the CpG loci indicated by a box. For each target gene, the native sequence of the region is shown in the top line and the sequences for unmethylated and methulated DNA as they would appear following bisulfite conversion and amplification are shown below. Unmethylated C-residues that would be converted by bisulfite and amplification to T residues are shown as Ts.

In some embodiments, the present invention provides use of a nucleic acid detection assay to coordinately analyze a plurality of the advantageous loci in a sample, thereby determining the disease state of cells in sample.

II. Determining methylation ratios for loci in the selected sequence(s). As discussed above, determining a methylation ratio for a locus comprises determining an average rate of methylation for that locus in a population of normal cells and determining the average rate of methylation at the same locus in a population of non-normal cells. As noted above, commonly used methods of methylation analysis of marker genes are performed on mixed nucleic acids, e.g., amplicons produced from unfractionated DNA from a mixed population of cells (such as DNA purified from a multicellular tissue sample). While some studies have analyzed individual clones of amplicons made from unfractionated sample DNA, the numbers of clones analyzed has typically been too small to reveal significant or reproducible differences in methylation ratios at individual CpG loci within the sequences. For example, in their comparison of highly rmethylated genes in colorectal cancer, Zou, et al., analyzed only six clones from each sample. (Zou, et al., Cancer Epidemiol Biomarkers Prev 2007; 16(12):2686), while Weisenberg, et al. used The present invention comprises large-scale analysis of individual DNA molecules, for example, by direct sequencing of individual DNA molecules, or by sequencing of clonally amplified DNA.

While not limiting the present invention to any particular methods, methods of clonally amplifying individual copies of nucleic acids (e.g., using PCR) can be used in the rapid analysis of large numbers of individual markers from normal and non-normal samples. Single-molecule amplification methods may comprise use of microchambers, emulsion reactions, “bridge PCR” on solid supports, or any of a number of established methods of segregating the amplification products arising from individual target molecules. Following single molecule amplification, amplicons can be sequenced.

Improved methods of sequencing individual molecules directly obviate the need to clone molecules into cells or, in some methods, the need to clonally amplify prior to sequencing. Elimination of cloning into cells makes analysis of much larger collections of molecules dramatically more efficient. Platforms for individual molecule sequencing include the 454 FLX™ or 454 TITANIUM™ (Roche), the SOLEXA™/Illumina Genome Analyzer (Illumina), the HELISCOPE™ Single Molecule Sequencer (Helicos Biosciences), the Ion Personal Genome Machine (Ion Torrent), and the SOLID™ DNA Sequencer (Life Technologies/Applied Biosystems) instruments, as well as other platforms still under development by companies such as Intelligent Biosystems and Pacific Biosystems. Although the chemistry by which sequence information is generated varies for the different next-generation sequencing platforms, all of them share the common feature of generating sequence data from a very large number of individual sequencing templates, in sequencing reactions that are run simultaneously. Data from the reactions are collected using, e.g., a flow cell, a chemical or optical sensor, and/or scanner, and sequences are assembled and analyzed using bioinformatics software.

In certain preferred embodiments, the present invention provides methods of analysis of methylation markers using digital sequencing to identify neoplasm-associated methylation loci that have methylation ratios that are statistically significantly advantageous compared to other loci in the same markers. In preferred embodiments, digital sequencing is done in a highly or massively parallel fashion, providing higher precision in identifying CpG methylation sites having advantageous methylation ratios.

For the massively parallel digital sequencing methods mentioned above, each molecule is analyzed for methylation at each CpG locus, so the percentage of DNA copies having methylation at any combination of the CpG loci can be analyzed after the experimental run. Further, each particular marker sequence, e.g., each target nucleic acid molecule, or clonal amplicon may be interrogated many, many times, e.g., at least 100 times, sometimes over 1000 times, and in some instances over 100,000 times, or as many as 500,000 times. Thus, patterns of coordinate methylation indicative of cancer or adenoma that would be undetectable in analysis of a handful of individual target molecules may be revealed.

III. Selection of a Subset of Methylation Loci for Coordinate Analysis

As noted above, determination of the methylation status of a set of CpG loci in a large number of copies marker DNA from both normal samples and non-normal samples (e.g., adenoma or cancer samples) reveals that certain CpG loci in marker genes or regions may tend to be coordinately methylated. Further, design of nucleic acid detection assays to interrogate a plurality of CpG loci for which coordinate methylation is indicative of adenoma or cancer in a sample can provide an assay that has improved signal-to-noise compared to assays that survey average percent methylation across entire marker genes.

One aspect of selecting a subset of CpG loci comprises selecting loci that have been determined to be coordinately methylated by use, e.g., of digital analysis methods. Another aspect comprises selecting CpG loci determined to have advantageous methylation ratios when normal DNA is compared to adenoma or cancer DNA. Assay designs may, but need not, make use of a CpG locus having the most advantageous methylation ratio compared to other loci in the same marker. In some embodiments, selection of a plurality of CpG loci as a subset comprises selecting the plurality of loci having the most advantageous methylation ratios. In other embodiments, selection of a plurality of CpG loci as a subset comprises selecting the locus having the most advantageous methylation ratio, then selecting at least additional CpG loci that are conveniently situated with respect to the first selected locus for the configuration of a particular nucleic acid detection assay (e.g., the selection of CpG loci having particular proximity to each other for configuring an invasive cleavage assay, ligation assay, amplification assay, etc.) in order to interrogate all of the selected CpG loci on copies of the target DNA in a single assay. In some embodiments, a candidate subset of CpG loci is further analyzed to determine the percentage of copies of marker DNA from non-normal samples that are coordinately methylated at those candidate loci, and that have little or no coordinated methylation in normal samples.

Analysis of Samples for Detection of Adenoma or Cancer

Conventional methods of methylation analysis, (e.g., conventional methylation-specific PCR, real time methylation-specific PCR, see, e.g., U.S. Pat. Nos. 5,786,146, 6,017,704, 6,200,756, 6,265,171), typically analyze in a non-digital fashion, e.g., analyzing a mixture of co-amplified molecules derived from a mixture of DNA target nucleic acids, so that analysis of the amplified products provides sequence information that reflects that aggregate or average methylation status in the amplicon population, but does not provide information on the percentage of starting molecules having coordinated methylation at all of a plurality of CpG loci. In some instances researchers have analyzed a number of cloned amplicons, which can reveal the diversity in methylation in the CpG loci within a target marker gene. However, sequencing individual clones has not provided enough data to reveal statistically significant coordinate methylation of specific subsets of CpG loci.

In contrast to conventional methods, we sought to analyze methylation marker genes in a massively parallel digital sequencing fashion to identify statistically significant coordinate methylation of specific CpG loci associated with neoplasms (adenoma and carcinoma). This method of analysis allowed us to:

1. Analyze samples for coordinate methylation in a marker gene as a means of detecting neoplasms without the need for testing any genetic (mutation) markers

2. Analyze samples for coordinate methylation in a plurality of marker genes as a means of detecting neoplasms without the need for testing any genetic (mutation) markers

We decided to use “digital” sequencing on a larger number of tissue samples obtained by biopsy from colorectal adenomas, colorectal cancers normal colorectal epithelia and other GI cancers and sequence a number of specific regions within several genes. This type of sequencing provides a methylation pattern for each individual methylated gene. For the first run we have 9 normal tissues, 38 adenomas and 36 cancer samples with the following markers—Vimentin, BMP3, Septin 9, TFPI2, 2 regions of LRAT, and EYA4.

Surprisingly we found that in some of the genes, the background observed as methylation in normal samples is randomly distributed in the sequences, while the methylation associated with cancer and adenoma is not. Thus, if certain rules are applied e.g. if all of C residues a, b, and c have to methylated in a diagnostic assay, then the number of DNA copies presenting methylation at all three positions in that sequence is reduced compared to the number of DNA copies displaying methylation at a subset of the positions. In some of the marker genes or regions tested, the reduction in number of DNA copies in normal DNA displaying methylation at all of the selected sites drops to a greater degree than it does in the DNA from cancer and/or adenoma sample, resulting in an significantly enhanced ratio of specific signal to background noise. For certain genes, the background from normal DNA is dramatically reduced by using multimethylation (coordinate methylation) analysis, while no equivalent reduction in signal from cancer and adenoma DNA is seen. For other genes the background in normal samples is less reduced and/or the signal from cancer DNA also decreases with multimethylation analysis, such that there is less or no net improvement in the signal-to-noise and the advantages of using a multimethylation analysis approached are less. Genes having favorable signal to noise in multimethylation analyses are readily determined empirically.

Tables 2A-2E show analyses of normal, adenoma, and cancer samples in which the average methylation was determined at each of the indicated CpG positions, in the indicated marker regions. For each marker, the numbered CpG positions are as indicated by the reference numbers in FIGS. 1A and 1B. The Mean methylation at each specific locus is shown at the bottom of each column for normal, adenoma, and cancer samples. The ratio of normal/mutant methylation for each locus (a methylation ratio at each locus) is shown at the bottom of each column of the Adenoma and Cancer sample data. The Mean columns on the right of each table indicate the average of methylation across all indicated CpG loci for each of the samples. The Mean and SD values across all normal samples at all loci are as indicated below each table of values from normal samples.

TABLE 2A Vimentin CpG 26 37 40 45 52 54 59 63 74 Mean NORMAL 8-4 11.63  6.08  6.62  3.52  3.65  2.31  3.44  3.66  3.88  4.98 8-5  6.92  3.4  2.97  1.26  1.19  0.5  2.61  0.97  1.14  2.33 8-6  12.25  8.92  5.06  4.01  2  2.45  2.27  3.44  5.21  5.07 8-7  15.74  9.41  5.7  6.72  3.73  5.26  5.92  4.29  4.46  6.80 8-8  6.48  2.52  2.71  2.72  4.26  3.14  1.67  2.67  2.84  3.22 8-9  5.65  2.48  1.27  1.48  1.5  0.87  1.7  2.02  3.18  2.24 8-10  5.36  2.77  1.09  2.84  2.09  0.85  1.89  1.97  2.55  2.38 8-11  7.5  2.65  2.34  1.66  4.9  4.84  1.71  3.5  3.04  3.57 8-12  5.91  5.12  3.52  1.91  2.53  2.27  1.84  1.86  1.58  2.95 Mean  8.60444  4.81667  3.47556  2.90222  2.87222  2.49889  2.56111  2.70889  3.09778  3.73 SD all  2.68 M + 2SD  9.09 M + 35D 11.77 ADENOMA 3-4 50.2 52.6 47 24.66 21.46 16.34 20.96 27.91 36.05 33.02 3-5 79.04 70.33 65.69 56.54 33.15 25.6 35.34 32.14 40.64 48.72 3-6 37.08 32.91 32.94 25.05 23.78 25.64 23.57 23.22 29.4 28.18 3-7 47.73 45.01 44.47 42.42 41.78 41.24 41.83 41.75 40.02 42.92 3-8 56.11 48.88 46.52 44.12 41.26 41.34 41.29 40.82 43.15 44.83 3-9  6.49  3.18  4.26  2.92  3.28  2.87  2.55  2.17  3.46  3.46 3-10  5.7  2.61  4.61  2.02  2.77  2.46  2.2  2.15  3.01  3.06 3-11 84.54 84.38 83.07 41.43 43.76 51.8 77.13 75.74 48.14 65.55 3-12 90.13 90.15 88.53 88.89 88.89 85.22 83.03 83.48 83.35 86.85 5-1 71.75 71.01 70.12 65.83 72.49 71.45 69.97 61.69 65.53 68.87 5-2 39.07 35.75 33.57 31.84 31.98 31.84 30.25 30.1 30.97 32.82 5-4 40.85 32.71 17.84 21.28 18.47 20.19 20.5 23.79 40.53 26.24 5-11 70.93 70.52 69.58 64.06 70.39 70.52 68.51 60.3 63.66 67.61 5-12 84.36 84.75 84.36 77.41 73.36 57.53 49.03 68.92 79.15 73.21 6-1 87.8 86.44 87.45 86.38 85.89 82.93 84.12 80.86 80.54 84.71 6-2 50.08 46.61 40.99 37.87 36.69 32.92 32.63 39.06 39.44 39.59 6-3 68.72 67.13 59.29 60.01 55.34 55.21 49.64 54.92 55.63 58.43 6-4 59.32 59.36 53.62 43.22 42.23 39.54 44.19 45.19 38.26 47.21 6-5 14.51  8.39  8.32  4.44  3.24  2.17  3.35  2.1  4  5.61 6-6 55.32 54.47 53.14 43.9 38.87 39.01 34.56 36.08 39.69 43.89 7-5 71.06 70.46 71.36 70.54 70.74 70.65 68.07 63.9 64.36 69.02 7-6 58.16 55.35 49.14 45.4 42.42 45.59 52.06 44.81 47.71 48.96 7-7 20.5 16.52 15.05 12.88 12 11.04 12.63 11.37 10.61 13.62 7-8 28.05 20.31 22.19 13.34 15.5 10.57 19.98 18.19 17.36 18.39 7-9 79.93 79.58 78.49 78.5 78.21 76.44 75.6 72.93 68.5 76.46 7-10 80.88 81.15 80.46 80.04 79.32 77.6 76.5 74.03 70.63 77.85 7-11 22.15 12.4 10.05  5.74  7.02  2.31  6.61  6.09  9.06  9.05 7-12 33.99 32.48 33.46 31.53 32.34 31.36 31.44 29.17 30.14 31.77 8-1 48.74 49.22 50.37 48.32 50.05 50.83 50.9 49.21 47.73 49.49 8-2 82.35 82.4 82.33 81.75 81.4 81.89 80.65 79.84 78.95 81.28 8-3 63.16 63.53 63.87 65.06 64.59 64.34 62.43 62.09 61.29 63.37 Mean 54.4742 51.9545 50.0690 45.0771 43.9571 42.5303 43.5974 43.3555 44.2245 N/M  0.15795  0.09271  0.06942  0.06438  0.06534  0.05876  0.05874  0.06248  0.07005 CANCER 1-1 55.95 46.38 39.19 39.35 39.25 38.19 30.93 26.81 33.13 38.80 1-2 46.07 48.21 39.2 26.64 33.32 21.46 34.99 27.12 35.62 34.74 1-3 36.4 23.79 25.7  5.9  6.22  4.63  4.54  4.83  6.72 13.19 1-4 80.52 66.55 81.59 80.93 80.51 81.01 79.67 70.78 76.98 77.62 1-5 86.12 85.72 85.71 85.99 85.66 86.21 84.61 81.46 82.86 84.93 1-6 76.07 75.26 76.89 75.46 74.46 75.74 74.47 70.81 57.16 72.92 1-7 11.26 13.06 26.2  7.2  5.52  2.47  3.57  2.68  3.34  8.37 1-8  3.24  2.4  3.04  3.05  3.85  2.63  4.58  3.05  2.85  3.19 1-9 91.73 91.67 90.09 90.69 91.4 87.87 88.51 72.29 75.96 86.69 1-10 91.81 91.44 90.57 91.05 91.41 88.25 88.74 71.95 75.29 86.72 1-11 96.17 95.74 94.21 85.46 66.31 55.84 45.36 53.05 55.95 72.01 1-12 81.46 70.09 58.61 49.52 40.5 39.07 40.96 34.12 29.02 49.26 2-1  3.2  2.71  2.37  2.63  4.47  1.65  3.08  2.32  2.89  2.81 2-2 27.81 27.74 28.16 24.33 27.01 26.02 25.55 19.5 21.84 25.33 2-3 50.74 50.46 50.99 50.06 50.1 50.5 48.51 47.2 46.41 49.44 2-4 49.58 48.5 49.13 42.19 48.89 40.01 47.42 35.7 43.83 45.03 2-5  5.4  3.66  3.69  3.1  3.06  2.56  2.54  2.58  4.21  3.42 2-6 39.3 39.04 39.7 38.65 39.38 38.32 37.14 36.43 36.53 38.28 2-7 55.16 54.9 54.89 54.26 55.2 55.07 54.09 51.47 51.4 54.05 2-8 62.61 62.07 63.43 61.37 62.91 62.71 62.75 58.15 57.97 61.55 2-9  5.64  4.28  1.97  2.06  3.81  2.26  6.47  2.86  2.97  3.59 2-10  4.96  3.92  1.36  1.52  3.48  2.24  5.74  1.94  2.31  3.05 2-11 68.03 67.01 59.74 66.53 68.15 64.62 65.4 57.5 61.65 64.29 2-12 60.01 47.56 57 32.01 44.13 40.93 33.52 30.48 45.93 43.51 3-2 45.95 45.94 46.16 45.91 45.76 46.43 44.81 43.34 42.49 45.20 5-3 30.38 29.55 29.08 28.84 30.73 30.97 28.13 27.42 28.37 29.27 5-5 29.85 17.44  6.57  7.31  3.29  3.22  2.62  2.62  5.57  8.72 5-6  9.19 15.6  3.9  4.11 10.38  3.48  2.23  9.96 13.16  8.00 5-7 52.6 51.71 39.08 34.92 25.56 28.83 38.34 45.32 48.14 40.50 5-8 21.65 20.69 20.69 19.08 22.4 21.76 21.76 19.51 19.4 20.77 5-9 44.3 42.83 43.32 39.74 44.14 41.53 28.83 39.41 40.88 40.55 5-10 43.44 41.51 42.53 40.36 42.76 40.94 28.62 37.06 39.34 39.62 6-7 91.08 90.06 90.06 90.28 89.75 90.85 88.49 84.97 85.04 88.95 6-8 65.53 62.59 59.24 59.03 51.16 50.98 53.92 58.67 60.01 57.90 6-9  7.35  4.62  4.49  2.5  3.93  4.24  5.55  4.66  8.91  5.14 6-10  5.56  3.22  4.2  1.36  2.97  2.07  3.03  2.9  7.64  3.66 6-11 66.81 65.15 64.79 64.41 67.47 63.55 64.79 60.5 61.13 64.29 6-12 68.38 63.74 45 34.86 35.79 25.68 31.84 25.16 33.91 40.48 7-1 63.71 61.07 60.24 55.62 56.07 57.06 45.3 48.45 49.69 55.25 7-2 49.63 41.8 31.28 30.46 23.63 26.56 15.68 23.23 24.1 29.60 7-3 61.72 60.06 60.77 60.47 60.53 60.58 59.07 57.15 55.66 59.56 7-4 83.11 82.56 77.6 81.19 67.36 56.23 74.45 56.05 69.23 71.98 Mean 48.3210 45.7690 44.1055 40.9619 40.7781 38.6957 38.3476 35.9871 38.2260 N/M  0.17807  0.10524  0.07880  0.07085  0.07044  0.06458  0.06679  0.07527  0.08104

TABLE 2B BMP3 CpG 34 53 61 70 74 Mean NORMAL 8-4 3.25 3.26 2.63 3.22 2.92 3.06 8-5 2.37 2.3 1.4 1.88 2.21 2.03 8-6 3.18 1.59 2.69 2.09 2.37 2.38 8-7 3.78 4.15 0.84 2.35 1.84 2.59 8-8 3.99 2.95 2.9 2.91 4.26 3.40 8-9 5.49 3.69 1.71 3.03 3.65 3.51 8-10 5.29 3.44 0.02 0.06 0.11 1.78 8-11 4.22 2.92 2.82 2.65 2.51 3.02 8-12 2.98 3.23 2.71 1.64 1.98 2.51 Mean 3.83889 3.05889 1.96889 2.20333 2.42778 2.70 SD all 1.18 M + 2SD 5.05 M + 3SD 6.23 ADENOMA 3-4 2.33 3.14 3.61 2.69 4.13 3.18 3-5 19.45 8 4 2.88 4.25 7.72 3-6 17.33 14.18 13.48 15.42 17.16 15.51 3-7 45.19 46.07 41.96 41.15 41.66 43.21 3-8 9.43 4.4 3.57 6.31 6.61 6.06 3-9 5.04 4.76 3.42 3.29 3.1 3.92 3-10 5.32 4.02 3.39 2.71 1.73 3.43 3-11 4.77 3.54 3.99 12.56 3.79 5.73 3-12 5.63 3.77 4.42 6.87 5.14 5.17 5-1 87.27 85.28 84.85 83.63 86.85 85.58 5-2 34.33 32.92 25.52 31.5 28.84 30.62 5-4 8.5 2.83 2.75 4.72 3.95 4.55 5-11 30.56 23.64 15.75 20.94 18.2 21.82 5-12 9.12 15.55 22.11 23.91 10.78 16.29 6-1 90.01 88.79 88.48 88.95 88.02 88.85 6-2 3.22 3.38 3.03 2.23 3.28 3.03 6-3 82.39 80.24 80.21 79.47 80.87 80.64 6-4 2.98 2.51 2.52 2.34 3.17 2.70 6-5 2.8 2.51 2.02 2.46 2.61 2.48 6-6 26.76 25.01 23.53 24.17 24.84 24.86 7-5 41.72 40.49 40.45 39.64 40.58 40.58 7-6 72.74 70.9 66.24 68.78 70.47 69.83 7-7 14.78 14.42 13.32 13 12.37 13.58 7-8 18.74 15.98 11.73 6.85 6.02 11.86 7-9 20.44 21.31 20.56 19.66 20.77 20.55 7-10 16.24 13.64 12.34 16.49 15.25 14.79 7-11 5.49 3.8 3.88 8.94 3.31 5.08 7-12 27.12 27.51 27.26 26.17 26.11 26.83 8-1 56.1 55.54 55.85 56.41 55.4 55.86 8-2 82.63 81.89 81.36 82.11 80.68 81.73 8-3 1.36 2.08 0.67 2.7 2.4 1.84 Mean 27.4126 25.8742 24.7184 25.7726 24.9142 N/M 0.14004 0.11822 0.07965 0.08549 0.09745 BMP3 CANCER 1-1 4.1 3.27 2.75 2.48 2.97 3.11 1-2 2.51 2.26 1.79 1.69 1.79 2.01 1-3 3.93 2.97 2.67 2.33 3.2 3.02 1-4 3.03 5.87 6.21 6.31 6.12 5.51 1-5 84.92 84.93 84.66 84.32 84.63 84.69 1-6 69.86 68.52 68.6 68.14 69.27 68.88 1-7 4.12 2.89 2.73 3.84 3.19 3.35 1-8 3.57 2.38 3 1.77 2.7 2.68 1-9 46.41 30.05 14.12 5.5 5.4 20.30 1-10 45.95 28.29 12.83 4.28 3.9 19.05 1-11 3.71 6.67 4.29 4.63 16.61 7.18 1-12 3.68 3.03 2.44 2.2 2.58 2.79 2-1 2.8 2.42 1.17 1.05 2.37 1.96 2-2 3.54 2.31 2.53 1.19 2.97 2.51 2-3 47.85 47.99 46.97 46.63 46.65 47.22 2-4 21.33 4.65 3.75 2.92 3.23 7.18 2-5 74.6 74.29 73.31 74.06 74.61 74.17 2-6 43.55 43.4 42.74 42.2 42.68 42.91 2-7 57.93 57.12 56.09 56.29 56.34 56.75 2-8 20.14 6 3.12 4.79 4.2 7.65 2-9 84.2 83.37 82.82 82.49 83.03 83.18 2-10 83.9 82.79 82.46 82.14 82.69 82.80 2-11 60.49 57.84 63.95 61.56 63.87 61.54 2-12 4.53 2.96 2.4 2.63 3.37 3.18 3-2 52.98 52.28 52.39 52.08 52.32 52.41 5-3 27.71 31.07 25.97 27.23 21.08 26.61 5-5 21.21 17.74 17 13.13 13.84 16.58 5-6 4.02 2.82 2.56 1.47 2.25 2.62 5-7 3.07 5.48 5.09 3.07 2.63 3.87 5-8 37.56 36.21 34.38 35.91 36.51 36.11 5-9 9.87 9.23 3.47 5.94 4.84 6.67 5-10 8.23 8.55 3.47 3.54 4.44 5.65 6-7 87.32 85.77 86.05 85.91 86.14 86.24 6-8 48.14 52.49 48.07 49.61 48.67 49.40 6-9 3.81 2.96 2.49 1.99 2.75 2.80 6-10 3.32 2.1 1.88 1.48 2.68 2.29 6-11 36.92 55.67 54.4 54.85 53.99 51.17 6-12 15.79 14.37 13.98 13.37 14.73 14.45 7-1 2.37 2.63 2.21 2.07 2.63 2.38 7-2 3.5 2.31 3.04 3.24 3.77 3.17 7-3 64.11 63.08 63.22 62.45 63.17 63.21 7-4 4.82 2.5 3.03 2.53 2.49 3.07 Mean 28.9381 27.4650 25.9548 25.3645 25.8881 N/M 0.13266 0.11137 0.07586 0.08687 0.09378

TABLE 2C Septin9 CpG 31 38 59 61 68 70 Mean NORMAL 8-4  4.33  4.68  3.65  3.4  3.05  2.55  3.61 8-5  5.81  5.22  3.88  2.55  2.56  2.62  3.77 8-6  9.7  8.48  6.03  5.02  5.4  3.5  6.36 8-7 25.07 23.83 15.73 15.38 12.7  7.3 16.67 8-8  9.6  9.34  7.07  7.68  7.06  5.31  7.68 8-9  2.93  3.79  2.85  2.19  2.67  2  2.74 8-10  2.95  3.94  2.22  3.17  3.03  2.33  2.94 8-11  9.91 10.36  7.98  6.95  6.22  4.78  7.70 8-12  7.37  8.04  5.18  3.9  3.49  3.71  5.28 Mean  8.63000  8.63111  6.06556  5.58222  5.13111  3.78889  6.30 SD all  5.06 M + 2SD 16.42 M + 35D 21.47 ADENOMA 3-4 71.21 71.25 67.4 67.63 70.36 70.48 69.72 3-5 78.85 78.62 78.74 79.41 78.69 77.51 78.64 3-6 47.45 47.25 47.36 46.33 45.52 43.7 46.27 3-7 54.28 54.19 53.89 54.5 52.93 51.27 53.51 3-8 82.73 80.42 82.52 83.81 81.98 83.04 82.42 3-9 59.76 59.11 59.97 59.88 59.03 59.65 59.57 3-10 59.64 59.13 60.14 60.14 59.08 59.62 59.63 3-11 81.43 80.79 81.85 82.38 81.68 81.7 81.64 3-12 87.2 87.14 87.78 85.16 87.49 86.78 86.93 5-1 81.86 81.66 82.4 81.83 81.39 82.63 81.96 5-2 58.22 58.48 57.93 56.61 55.53 52.77 56.59 5-4 90.56 89.44 91.06 90.32 89.35 89.37 90.02 5-11 82.52 81.57 82.17 82.04 80.93 80.54 81.63 5-12 87.76 87.67 88.6 87.5 87.52 83.04 87.02 6-1 88.48 88.58 88.91 88.89 88.14 87.99 88.50 6-2 54.02 53.55 52.96 51.62 51.85 49.39 52.23 6-3 75.28 74.77 75.14 74.75 73.85 70.73 74.09 6-4 66.87 67.68 66.53 55.84 46.89 43.19 57.83 6-5 39.2 39.97 29.71 29.79 24.9 18.71 30.38 6-6 46.52 46.87 47.66 46.69 46.41 43.43 46.26 7-5 71.84 71.15 71.81 71.76 71.65 70.46 71.45 7-6 67.38 67.19 66.38 67.37 67.74 65.9 66.99 7-7 39.96 39.74 40.16 38.96 38.06 35.38 38.71 7-8 76.27 73.19 78.23 77.87 70.99 55.35 71.98 7-9 82.36 81.58 82.87 80.95 81.01 79.39 81.36 7-10 82.1 81.35 83.09 81.42 80.71 79.28 81.33 7-11 79.96 79.14 76.53 75.27 74.93 63.99 74.97 7-12 28.03 27.75 28.58 28.03 27.64 27.52 27.93 8-1 47.19 46.29 48.75 47.99 47.07 47.31 47.43 8-2 78.05 78.32 78.79 79.02 77.53 78.26 78.33 8-3 73.6 73.1 73.21 73.63 72.92 73.09 73.26 Mean 68.4058 67.9658 68.1006 67.3352 66.2506 64.2410 67.0498 N/M  0.12616  0.12699  0.08907  0.08290  0.07745  0.05898  0.09403 CANCER 1-1 74.24 74.37 74.74 69.16 73.67 69.78 72.66 1-2 64.3 63.59 64.48 63.97 64.03 64.55 64.15 1-3 78.49 77.13 77.94 75.03 76.7 75.33 76.77 1-4 76.88 76.83 61.04 77.26 77.25 76.34 74.27 1-5 83.22 82.77 82.91 83.1 78.22 81.8 82.00 1-6 72.72 72.6 73.26 73.34 72.55 72.53 72.83 1-7 58.64 58.8 59.5 59.02 57.95 58.03 58.66 1-8 41.04 41.59 42.34 39.9 39.31 31.59 39.30 1-9 87.69 87.79 87.9 85.22 87.34 86.76 87.12 1-10 87.52 88.04 88.3 85.78 87.66 87.37 87.45 1-11 93.45 93.07 83.24 92.82 92.33 92.82 91.29 1-12 54.85 51.7 55.46 53.56 51.61 48.38 52.59 2-1 59.5 59.92 61.38 60.56 58.72 50.88 58.49 2-2 52.9 57.7 53.1 57.04 57.76 46.02 54.09 2-3 49.88 49.48 50.03 50.05 48.88 49.32 49.61 2-4 45.75 46.15 46.94 47 46.06 29.99 43.65 2-5 65.13 65.64 67.05 67.26 66.23 66.47 66.30 2-6 20.94 20.51 20.35 19.83 20.44  9.66 18.62 2-7 47.93 46.04 41.52 41.4 36.64 39.59 42.19 2-8 68.51 68.14 69.55 69.26 68.23 68.64 68.72 2-9 75.02 73.17 56.65 75.24 71.97 75.17 71.20 2-10 74.22 72.24 56.32 74.72 71 74.53 70.51 2-11 37.38 38.85 24.02 14.56 10.66  5.93 21.90 2-12 59.19 58.71 59.1 58.62 55.6 54.69 57.65 3-2 46.5 46.36 47.19 48.3 45.89 45.97 46.70 5-3 47.82 48.26 49.38 48.05 48.32 48.3 48.36 5-5 30.03 28.6 30.72 27.46 28.69 29.34 29.14 5-6 64.74 65.65 65.21 60.89 64.87 65.19 64.43 5-7 57.61 58.27 59.96 58.38 58.15 54.59 57.83 5-8 30.81 30.44 32.62 31.11 31.36 31.13 31.25 5-9 57.09 57.39 58.38 57.84 56.92 57.5 57.52 5-10 56.43 56.72 58.5 57.27 56.35 56.66 56.99 6-7 86.96 86.4 81.86 86.91 86.55 84.06 85.46 6-8 61.93 60.83 61.43 62.11 62.02 62.04 61.73 6-9 55.17 54.87 55.62 55.64 55.6 55.37 55.38 6-10 54.31 54.02 54.91 54.51 54.35 54.33 54.41 6-11  2.04  2.13  3.89  3.2  2.29  2.48  2.67 6-12 75.22 75.08 76.44 76.09 75.94 76.3 75.85 7-1 63.33 63.35 63.77 63.97 62.75 59.07 62.71 7-2 63.88 62.96 64.86 63.63 63.36 62.23 63.49 7-3 73.04 73.06 73.73 74.34 73.65 73.43 73.54 7-4 75.73 77.28 76.63 79.06 79.23 79.13 77.84 Mean 60.2864 60.1548 58.8624 59.5824 58.9786 57.4593 N/M  0.14315  0.14348  0.10305  0.09369  0.08700  0.06594

TABLE 2D TFPI2 CpG 28 33 41 50 55 59 63 67 74 Mean NORMAL 8-4 16.8 11.72 18.34 15.92 14.43 12.65 20.44 11.46 10.88 14.74 8-5 10.4  6.72 10.5  9.92  8.66  8.57 14.69  7.26  6.75  9.27 8-6  5.78  3.74  5.21  5.57  4.66  5.04  7.19  2.99  4.17  4.93 8-7  7.42  3.99  6.42  6.28  5.29  4.82  8.15  3.56  3.07  5.44 8-8  5.19  3.55  3.84  3.46  2.28  2.69  5.08  2.4  2.54  3.45 8-9  9.18  6.49 10.3  7.05  6.96  6.93 11.24  4.28  4.52  7.44 8-10  9.2  5.94 10.48  7.21  6.49  6.71 11.22  4.83  4.22  7.37 8-11  8.32  3.01  5.69  6.61  3.66  4.03  6.71  3.07  3.07  4.91 8-12  5.23  3.81  5.83  4.96  3.17  2.96  7.42  3.75  2.28  4.38 Mean  8.61333  5.44111  8.51222  7.44222  6.17778  6.04444 10.23778  4.84444  4.61111  6.88 SD all  3.86 M + 2SD 14.61 M + 35D 18.47 ADENOMA 3-4 66.66 49.94 65.74 66.29 59.94 62.71 64.28 48.29 30.53 57.15 3-5 77.23 76.12 77.55 76.96 75.45 73.61 76.39 72.7 71 75.22 3-6 41.89 36.35 40.76 40.97 40.56 38.13 40.11 37.77 34.48 39.00 3-7 52.43 48.78 52.34 53.57 50.9 49.98 52.74 47.83 44.76 50.37 3-8 80.17 79.31 79.2 79.83 79.76 77.16 76.28 78.09 64.06 77.10 3-9 41.24 39.22 40.04 40.87 40.69 40.06 39.84 39.23 39.04 40.03 3-10 39.75 37.16 38.59 39.38 39.06 38.61 38.26 37.68 36.9 38.38 3-11 84.09 81.71 83.36 83.58 83.66 82.34 81.57 82.53 80.87 82.63 3-12 86.28 75.8 86.62 87.69 78.28 79.03 86.59 79.89 58.94 79.90 5-1 78.42 77.48 78.38 73.72 77.69 76.86 72.05 75.7 70.17 75.61 5-2 59.47 53.62 57.42 53.97 55.91 55.91 54.51 52.46 50.29 54.84 5-4 91.15 91.11 92.16 87.49 91.68 86.88 86 90.22 89.08 89.53 5-11 73.27 70.21 70.36 68.17 69.56 68.83 66.21 69.63 69.41 69.52 5-12 78.05 75.39 84.77 80.16 80.59 83.95 78.32 83.67 83.63 80.95 6-1 86.13 84.43 85.44 85.9 85.74 83.62 83.43 84.01 82.29 84.55 6-2 52.6 48.51 50.23 51.37 50.91 48.11 50.74 48.74 47.62 49.87 6-3 71.67 68.75 70.6 70.04 70.4 67.48 69.53 67.59 64.92 69.00 6-4 53.89 27.64 52.9 53.58 47.65 29 48.93 48.09 39.87 44.62 6-5 18.96 10.51 19.35 14.32 13.37 12.03 19.15  7.52  6.76 13.55 6-6 52.19 48.46 50.11 50.18 50.03 47.91 49.4 47.35 40.79 48.49 7-5 57.72 56.03 57.33 57.64 57.01 55.73 56.81 55.51 53.79 56.40 7-6 52.68 49.32 51 50.35 50.47 48.95 48.43 49.85 49.02 50.01 7-7 25.22 18.22 21.12 20.96 19.79 18.46 22.15 16.57 15.59 19.79 7-8 33.85 15.37 23.72 19.92 27.03 37.42 54.82 30.88 33.69 30.74 7-9 73.9 59.02 70.99 65.87 66.99 61.66 71.3 62.11 56.11 65.33 7-10 71.51 56.26 68.66 63.4 64.27 58.32 69.59 60.46 55.45 63.10 7-11 63.86 52.6 68.02 62.26 55.02 53.43 67.1 48.71 39.43 56.71 7-12 26.9 21.82 25.75 26.12 26.72 24.49 27.01 22.98 22.07 24.87 8-1 39.67 36.94 38.87 39.82 38.12 37.04 39.47 36.65 35.48 38.01 8-2  4.39  2.77  3.43  9.4  3.36  4.15  5.24  5.01  4.29  4.67 8-3 18.38  6.67  3.77  8.39  7.12  8.05 22.38  3.59  7.32  9.52 Mean 56.5684 50.1781 55.1155 54.2635 53.4752 51.9326 55.4397 51.3326 47.6661 N/M  0.15226  0.10844  0.15444  0.13715  0.11553  0.11639  0.18467  0.09437  0.09674 CANCER 1-1 75.92 73.75 76.91 76.98 76.89 75.12 74.58 75.44 71.99 75.29 1-2 60.7 58.48 59.83 60.11 59.24 58.56 58.47 58.39 58.02 59.09 1-3 69.4 67.39 69.05 69.3 69.58 66.17 67.34 67.9 66.92 68.12 1-4 84.99 82.51 85.32 85.75 83.16 80.99 83.51 54.26 56.55 77.45 1-5 80.24 78.4 79.98 77.16 79.53 79.17 78.2 72.3 76.71 77.97 1-6 72.82 70.18 72.1 72.24 72.46 71.33 70.86 69.43 69.23 71.18 1-7 56.49 13.25 31.78 55.62 33.25 30.17 54.09 28.21 32.74 37.29 1-8 32.13 20.84 30.98 32.68 33.26 30.28 30.66 29.42 29.77 30.00 1-9 89.35 86.99 84.73 89.13 88.76 87.47 86.92 87.1 60.92 84.60 1-10 88.63 86.02 83.65 88.27 87.98 86.9 86.05 86.28 60.23 83.78 1-11 70.4 12.47 93.04 93.91 75.62 91.84 90.9 64.22 18.62 67.89 1-12 67 65.56 66.77 66.96 66.98 65.79 64.81 64.2 64.96 65.89 2-1 15.11 11.81 12.62 12.17 13.99 11.93 12.78 12.63 11.69 12.75 2-2 28.93  6.35 10.73 33.33 17.12 15.95 30.68  8.02  7.15 17.58 2-3 45.41 43.39 44.33 44.06 44.47 43.25 42.52 43.4 42.42 43.69 2-4 45.2 42.77 44.85 45.04 45.13 43.42 43.83 41.69 35.59 43.06 2-5 63.22 51.49 65.06 65.35 65.29 63.52 63.19 62.25 52.05 61.27 2-6 31.68 28.87 29.77 30.31 30.84 29.71 29.63 29.67 29.19 29.96 2-7 45.62 43.81 44.36 44.25 41.67 43.54 43.71 42.12 41.39 43.39 2-8 64.7 62.61 63.61 63.79 57.58 60.84 60.93 62.33 58.59 61.66 2-9 75.87 71.86 75.47 74.9 75.49 71.68 71.42 74.03 73.19 73.77 2-10 72.47 68.98 72.08 71.33 72.4 68.8 67.93 70.49 69.81 70.48 2-11 59.41 50.01 64.27 64.23 56.2 58.22 60.85 58.34 39.96 56.83 2-12 50.9 45.83 56.05 55.83 52.73 51.94 54.45 52.23 48.45 52.05 3-2 43.65 40.73 43.07 42.99 42.94 43.01 42.86 40.76 40.71 42.30 5-3 38.56 30.45 42.56 40.53 35.54 34.59 40.19 36.63 28.62 36.41 5-5 61.12 56.82 57.86 54.72 56.43 56.68 54.83 50.83 54.88 56.02 5-6 53.8 48.22 59.5 38.46 17.73 19.02 28.45 18.28 13.88 33.04 5-7 63.5 60.54 61.93 58.59 62.75 60.84 58.35 58.15 58.39 60.34 5-8 22.3 18.7 19.36 18.14 20.25 18.93 19.06 19.85 19.06 19.52 5-9 52.64 51.06 52.01 48.3 50.75 49.65 47.16 46.77 49.25 49.73 5-10 49.62 48.7 48.35 45.2 48.18 47.48 46.33 44.56 46.76 47.24 6-7 84.49 83.96 84.44 83.97 84.53 79.72 81.88 82.5 81.27 82.97 6-8 52.41 45.12 51.33 50.98 50.21 49.08 50.3 45.16 39.78 48.26 6-9 34.12 12.99 37.31 25.11 14.81 25.65 37.63 14.79 31.82 26.03 6-10 34.11 12.42 36.31 23.71 13.2 24.85 37.09 14.15 31.6 25.27 6-11 61.62 30.74 60.7 61.09 59.77 59.31 58.74 59.47 58.63 56.67 6-12 57.77 42.67 65.73 64.01 62.69 63.07 64.1 61.68 49.37 59.01 7-1 62.23 60.07 61.28 61.27 62.01 57.54 59.89 60.22 59.01 60.39 7-2 38.32 36.07 41.16 40.3 38.66 33.41 39.09 37.17 35.3 37.72 7-3 55.98 53.42 54.54 54.6 54.66 53.01 53.42 53.37 51.96 53.88 7-4 76.63 56.91 76.83 76.78 57.28 71.59 73.28 65.43 54.48 67.69 Mean 56.8919 48.4098 56.4669 56.2250 53.1431 53.1910 55.2610 50.5743 47.1645 N/M  0.15140  0.11240  0.15075  0.13237  0.11625  0.11364  0.18526  0.09579  0.09777

TABLE 2E EYA4 CpG 27 29 31 34 37 44 46 55 65 74 Mean NORMAL 8-4  7.95  3.92  5.81 15.44 11.76 10 11.72 14.57  6.23  6.67  9.41 8-5  0.52  0.72  5.76  3.59  0.23  2.35  3.48  0.41  2.95  0.38  2.04 8-6  2.46  2.56  2.74  2.51  2.84  0.1  2.47  3.88  2.24  2.18  2.40 8-7 27.16 27.12  7.42 26.64 26.48  4  4.06 10.23 23.1 25.77 18.20 8-8  3.46  6.25  0.63  5.31  5.58  6.13  9.73  4.37  4.21 10.11  5.58 8-9  1.55  1.81  0.51  1.55  1.55  0.23  0.26  1.66  6.07  8.5  2.37 8-10  0.18  0.7  0.37  0.2  0.18  0.17  0.42  0.18  1.71  7.13  1.12 8-11 14.45  9.34  8.79 11.49 11.46  8.65  3.08  8.6  2.32 10.83  8.90 8-12  3.31  3.92  2.34  8.86  2.6  4.66  2.22  9.79  0.06  1.56  3.93 Mean  6.78222  6.26000  3.81889  8.39889  6.96444  4.03222  4.16000  5.96556  5.43222  8.12556  5.99 SD all  6.64 M + 2SD 19.27 M + 35D 25.90 ADENOMA 3-4 42.34  7.24 13.95 15.1 34.99 13.74 20.19 23.02 14.87 25.63 21.11 3-5 75.62 55.47 57.53 79.82 76.73 47.75 54.51 49.28 29.16 62.02 58.79 3-6 94.03 94.1 93.31 90.55 87.59 92.01 88.36 66.23 38.32 65.91 81.04 3-7 95.43 96.07 95.59 95.71 95.27 92.45 93.84 89.14 68.37 72.86 89.47 3-8 95.86 86.62 49.24 95.35 79.12 96.8 94.79 92.04 63.24 70.73 82.38 3-9 14.55 23.73 32.45 24.76 18.23 11.68 13.59 23.87  8.92 47.61 21.94 3-10 13.57 23.23 29.82 21.68 16.82 10.17 13.21 21.92  7.26 44.63 20.23 3-11 97.79 98.59 97.25 97.61 82.98 94.67 96.87 43.46 62.52 84.91 85.67 3-12 65.73 63.48 64.89 71.19 67.65 69.9 72.69 81.74 39.26 68.67 66.52 5-1 90.16 93.09 89.89 95.48 81.65 33.24 91.22 76.6 56.12 76.33 78.38 5-2 83.81 71.43 60.95 87.62 79.52 27.62 79.52 58.1 45.71 66.19 66.05 5-4 28.96 38.8 27.69 64.66 61.75 13.3 38.62 14.57 12.93 48.45 34.97 5-11 94.41 94.41 92.31 97.2 90.91 28.32 76.92 66.08 39.86 80.07 76.05 5-12 98.79 96.76 91.09 97.17 91.9 11.74 58.3 25.51 36.44 86.64 69.43 6-1 95.63 96.8 96.26 96.51 94.84 94.08 96.72 89.86 67 71.73 89.94 6-2 89.62 87.37 86.95 88.6 83.53 86.34 83.06 83.31 52.22 62.18 80.32 6-3 97 96.79 95.1 96.68 95.73 95.3 94.9 82.6 59.33 71.11 88.45 6-4 47.71 28.77 29.19 83.03 38.35 14.73 24.06 15.42  5.76 29.73 31.68 6-5 15.49  7.45  9.53 20.3 13.44  8.9  5.48 15.51  6.9 11.4 11.44 6-6 85.06 85.26 84.76 85.88 84.14 85.08 84.83 79.57 64.17 63.75 80.25 7-5 93.98 94.73 95.05 92.77 94.15 92.06 90.95 87.51 60.32 65.65 86.72 7-6 92.4 93 92.47 92.76 83.21 91.36 87.67 86.05 63.1 64.39 84.64 7-7 61.1 61.34 59.78 65.15 62.99 59.16 60.7 52.74 37.07 44.69 56.47 7-8 17 11.63  8.37 25.03 17.31  5.2  4.95 12.33  3.31 50.91 15.60 7-9 80.91 74.06 77.26 82.16 76.42 76.69 77.13 72.52 52.25 65.57 73.50 7-10 77 71.06 70.81 78.33 72.76 78.15 74.08 69.86 51.78 64.53 70.84 7-11  0.53  0.84  9.01  2.12 16.04  6.46  9.41  8.97 12.98 22.93  8.93 7-12 73.35 76.12 76.18 71.6 75.34 74.97 76.41 64.9 42.94 51.83 68.36 8-1 89.74 89.94 90.16 89.41 89.4 89.69 88.68 74.41 70.39 76.14 84.80 8-2 96.69 96.5 98.29 97.88 92.95 97.53 97.08 79.52 75.3 83.15 91.49 8-3 89.79 93.92 94.97 94.28 71.33 59.43 61.38 34.46 42.65 55.09 69.73 Mean 70.7758 68.0194 66.7774 74.0771 68.6142 56.7265 64.8426 56.1645 41.6274 59.8526 N/M  0.09583  0.09203  0.05719  0.11338  0.10150  0.07108  0.06416  0.10622  0.13050  0.13576 CANCER 1-1 95.77 95.43 95.77 96.12 90.28 94.76 94.95 72.84 67.49 82.09 88.55 1-2 91.26 92.07 92.65 93.03 91.62 91.94 91.19 69.82 62.06 79.13 85.48 1-3 95.81 96.96 96.22 95.96 94.14 96.56 94.2 80.24 62.51 77.93 89.05 1-4 96.31 95.85 97.08 96.12 87.07 94.71 76.53 36.85 36.16 69.45 78.61 1-5 97.36 97.86 97.2 97.58 97.13 97.41 97.29 84.8 65.65 81.89 91.42 1-6 94.33 95.64 95.97 95.08 92.67 95.52 93.72 69.51 72.14 82.97 88.76 1-7 18.72  9.23  9.21 53.23 36.24 14.01 44.41 58.09  7.14 13.19 26.35 1-8 46.66 30.42 29.64 59.15 24.97 19.9 15.52 10.52 31.39 67.42 33.56 1-9 96.11 94.22 93.91 97.09 90.15 93.97 93.78 73.93 73.3 67.96 87.44 1-10 96.21 94.28 93.99 97.24 91.01 94.45 94.19 73.8 72.18 70.03 87.74 1-11 98.77 97.62 97.6 96.75 97.25 98.23 96.89 93.56 41.81 78.11 89.66 1-12 91.41 91.71 92.15 92.58 91.12 91.65 69.38 67.07 55.93 78.28 82.13 2-1 19.91 21.85 20.02 19.4 20.66 18.45 19.59 16.34 12.97 12.04 18.12 2-2 33.9 37.54 33.6 35.41 26.72 32.37 30.33 25.63 12.73 21.64 28.99 2-3 87.09 85.4 86.41 87.06 70.66 82.49 87.01 52.21 38.48 67.66 74.45 2-4 81.28 84.94 84.36 84.62 81.79 75.22 67.07 18.47  8.36 20.2 60.63 2-5 95.51 84.65 89.45 97.75 96.96 89.4 93.64 71.6 60.1 71.7 85.08 2-6 89.64 90.39 92 89.4 86.3 86.95 89.9 78.41 50.38 66.56 81.99 2-7 87.95 83.38 87.88 86.33 79.7 62.37 74.51 58.32 41.49 63.64 72.56 2-8 88.93 79.13 74.75 91.49 78.51 87.98 88.76 67.95 49.06 66.23 77.28 2-9 96.63 96.37 96.75 95.33 94.76 91.66 91.7 82.93 62.73 69.07 87.79 2-10 96.23 96.18 96.45 95.35 94.09 91.68 92.03 84.12 62.29 68.48 87.69 2-11 78.21 91.62 93.38 82.83 80.84 89.21 86.42 47.48 19.08 55.01 72.41 2-12 78.92 77.21 65.25 88.2 87.17 48.02 48.4 43.86 32.71 55.41 62.52 3-2 83.19 83.33 85.51 87.83 84.07 85.4 83.93 72.44 38.71 62.51 76.69 5-3 75 77.63 73.03 76.97 65.13 21.71 66.45 14.47  8.55 28.29 50.72 5-5 76.69 89.83 90.89 79.24 49.15 28.39 67.37 31.78 19.07 40.04 57.25 5-6 84.14 66.9 82.76 80.69 60.69 18.62 65.52 26.9 13.79 33.1 53.31 5-7 83.85 83.08 85 83.46 76.54 29.23 84.23 41.92 30.38 70 66.77 5-8 59.34 63.74 62.09 68.13 56.59 23.08 62.09 39.01 27.47 46.7 50.82 5-9 82.26 85.35 86.38 84.32 80.21 29.56 81.23 18.25 26.74 74.29 64.86 5-10 78.22 81.24 81.41 80.57 78.06 25.46 79.23 18.76 23.95 71.36 61.83 6-7 96.25 97.94 97.18 96.54 94.24 95.22 96.77 90.47 68.78 71.86 90.53 6-8 75.42 60.15 66.23 85.35 70.66 76.79 71.49 67.79 49.75 59.12 68.28 6-9 74.63 72.85 75.4 77.97 75.44 66.67 58.5 43.07  8.53 12.99 56.61 6-10 72.13 68.52 70.34 73.37 69.14 63.44 53.88 42.34  6.23 11.98 53.14 6-11 90.57 91.22 90.96 88.64 90.51 87.87 90.7 78.51 60.18 62.03 83.12 6-12 81.63 94.2 95.03 93.02 79.24 89.94 88.49 58.09 32.59 53.01 76.52 7-1 95.77 93.81 90.2 96.4 94.38 94.6 93.33 85.21 55.69 65.68 86.51 7-2 69.02 62.6 67.89 67.45 67.11 63.19 61.07 42.99 27.53 29.84 55.87 7-3 87.53 87.41 85.13 87.34 86.47 85.59 86.17 77.97 56.43 57.76 79.78 7-4 96.06 96.57 94.2 94.28 78.02 15.36 26.08 27.32 17.28 53.07 59.82 Mean 81.3005 80.3886 80.7457 83.9207 77.0824 67.5960 74.9510 55.1343 39.7569 56.8981 N/M  0.08342  0.07787  0.04730  0.10008  0.09035  0.05965  0.05550  0.10820  0.13664  0.14281

From the multimethyation data presented herein (see, e.g., Table 2) it is possible to:

-   -   a. Identify regions within the gene sequences that give higher         discrimination between normal and non-normal (e.g., cancer and         adenoma) cells;     -   b. Identify particular genes that have greater signal-to-noise         (non-normal cell signal compared to normal cell background);     -   c. Identify particular methylation loci that have greater         signal-to-noise;     -   d. Identify particular methylation loci that are coordinately         methylation in adenoma and cancer samples but not in normal         samples, such that detection coordinate methylation at these         loci is a sensitive indicator of adenoma or cancer;     -   e. Identify genes with very low background methylation allowing         for greater dilution of methylated DNA in normal DNA with less         decrease in assay sensitivity;     -   f. Identify genes that are diagnostically complementary to each         other, i.e., that when analyzed in combination produce         diagnostic information of elevated sensitivity and/or elevated         specificity compared to the genes analyzed alone.     -   g. Identify combinations of genes that give elevated sensitivity         at elevated specificity, e.g., 100% sensitivity for cancer and         adenoma at 100% specificity.

EXPERIMENTAL EXAMPLES Example 1 Use of Digital PCR and Sequencing for the Identification of Specific Subsets of CpG Loci Methylated in Cancer and Adenoma Samples

DNA extracted from frozen tissue samples was treated with an EPITECT bisulfite conversion kit (Qiagen) to convert non-methylated cytosines to uracil. Methylated cytosines remain unconverted. Primers for each gene region were designed for each sequence such that the composition of the amplification products remained the same as the original target sequences and methylated and non methylated sequences were amplified with equal efficiencies. Amplification of the dU-containing converted DNA produced amplicons having T-residues in place of the dU residues. The amplicons were then prepared for sequencing on the Illumina instrument. For each tissue sample, the amplification reaction for each target was prepared from the same sample of bisulfite-treated DNA.

After sequencing, the data was analyzed quantitatively as an average methylation similar to Sanger sequencing, but at a higher precision and resolution in that the combined signal at each position is calculated from individual molecules. For each amplicon sequence, a set of CpG loci was evaluated for percent methylation in the different tissues, to identify a subset the loci that were co-methylated more frequently in cancer and/or adenoma samples than in normal tissues.

Illumina Sequencing Protocol:

Sequencing was conducted according to the procedure recommended for the Illumina Genome Analyzer IIx, GAIIx, Data collection software ver. 2.5, and Pipeline analysis software ver 1.5. Briefly, the Illumina procedure comprises a) preparation of a library from sample DNA by attachment of known sequence tags that permit indexing, flow cell attachment, amplification, and sequencing; b) attachment of the library to a flow cell surface; c) bridge amplification to produce clusters of DNA fragments derived from single molecules, and d) sequencing in using iterative primer extension reactions using labeled reversible terminators to determine the nucleotide sequence of each cluster of amplicons. See, e.g., Bentley, et al, Nature 456, 53-59 (6 Nov. 2008)/doi:10.1038/nature07517 with supplementary methods and data, incorporated herein by reference. Use of unique tag sequences for indexing permits analysis of multiple samples in a single flow cell. See, e.g., Craig, et al., Nat. Methods Nat Methods. 2008 October; 5(10):887-93 (Epub 14 Sep. 2008), incorporated herein by reference.

Sample Set:

N=82, composed of tissue DNA extracted from 42 colorectal cancers, 31 pre-cancerous adenomas and 9 normal colonic mucosa.

Flow Cell Configuration

Samples were indexed at 12 per lane for a total of 7 lanes. A flow cell is composed of 8 lanes, one of which is dedicated to a phiX quality control.

Library Preparation:

Tissue-extracted DNA from patients was bisulfite-treated and a 2-step amplification using approximately 10,000 genome copies of initial material was carried out. The first round used tailed (T1)(Illumina) primers specific for marker sequences. These tails were Illumina-derived sequences needed for round two. The second round (T2)(Illumina) PCR uses primers specific for the Illumina tails added in T1, and incorporates the index, sequencing primer, and flow cell attachment sequences. During the library preparation, multiple qPCR checks were run on the samples to ensure equimolar representations of all amplicons in the libraries.

Primer Design:

Forward and reverse primers specific for regions with converted, non-CpG cytosines are designed (using, e.g., MethPrimer software) to amplify each of the specific biomarker sites in a non-methylation specific manner. When CpG cytosines cannotbe avoided in the primer design, degenerate mixtures (C/T; G/A) are used those sites in the primers. If additional sequences outside the primary amplicon need to be queried, additional primers may be designed. If CpGs in the target sequence cannot be avoided, the primers may incorporate degenerate bases at CpG sites (BiSearch software).

Primers for second round PCR comprise sequences for Illumina flow cell attachment (bridge amplification sites), sequencing primer sites (for the sample read), index sites, and sequencing primers sites (for the indexing read). Each of the primer sets (x) has 12 different index tags, for a total of 12× sets. Index-independent primer sets (n=x) are optimized on converted non-methylated DNA (e.g., human DNA) and converted methylated DNA. For example, the control DNAs are amplified, purified (e.g, using AMPURE treatment (Agencourt)), and run on an Agilent 2100 Bioanalyzer to assess the size and quantity of the amplified nucleic acids.

Experimental Steps:

DNA Isolation and Bisulfite Conversion:

-   1) DNA was extracted and purified from tissue using either DNAZOL     (Invitrogen), or QIAAMP kits (Qiagen) and the concentration and     purity was measured by absorbance (A230/A260/A280) using a Nanodrop     ND-1000 spectrophotometer (Thermo Scientific). PICOGREEN     fluorescence (Molecular Probes) was used in conjunction with a TECAN     F-200 (Tecan) plate-reader for high samples exhibiting high A230     values. -   2) Samples were adjusted to a concentration of at least 200 ng/uL     using a Speedvac evaporation concentrator (Thermo Scientific), as     necessary. -   3) For each sample, 2 ug of DNA was bisulfite-treated using EPITECT     96-well plates (Qiagen). -   4) Recovery was assessed by absorbance and OLIGOGREEN fluorescence     (Molecular Probes) and the conversion efficiency was assessed with     quantitative PCR using cytosine-containing, non-CpG primers specific     for unconverted DNA. Conversion efficiency was determined to be     greater than 99%     First-Round PCR: -   5) The 84 samples were amplified in reactions with 30 ng DNA using     marker-specific (T1) primer sets. The number of cycles used was     specific for each marker set and was empirically determined by the     initial control reactions on both methylated and unmethylated DNA.     The number of cycles is approximately set at the mean calculated Ct     value. For example, the following numbers of cycles were used for     the indicated markers:     -   TFPI12; 26 cycles     -   SEPT9; 27 cycles     -   BMP3; 28 cycles     -   VIM; 28 cycles     -   EYA4; 29 cycles -   6) The amplified product from each reaction was purified using     AMPURE beads (Agencourt), with elution in EB buffer (Qiagen). -   7) The product for each marker was quantified by qPCR as described     above, using a T2 primer set. Master plates were prepared containing     equal amounts of each biomarker for each sample.     Second-Round PCR: -   8) The first-round samples were then amplified with the 12 T2     indexed primers. -   9) The product for each reaction was again purified and     concentrations measured with qPCR, this time with flow-cell-specific     primers and a standard curve created using serial dilution of PhiX     control DNA.     Final Library Prep: -   10) The 12 columns of each plate were combined into 1 master column     in equimolar proportions. 1 uL of each library was loaded on a high     sensitivity DNA chip (Agilent) and run on the Bioanalyzer. A final     qPCR was also performed on the 480 LightCycler (Roche) with the PhiX     standards. -   11) The libraries were sequenced on the Illumina Instrument and the     sequence data obtained for each marker for each sample. -   12) Average methylation at each CpG site was calculated for each     marker for each sample. See Table 2. -   13) The percentage of molecules methylated at all of the CpG loci in     a defined subset of the CpG loci in each marker was calculated for     each marker for each sample. See FIGS. 4A-4I.

All publications and patents mentioned in the above specification are herein incorporated by reference. Various modifications and variations of the described methods and systems of the invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention that are obvious to those skilled in the relevant fields are intended to be within the scope of the following claims. 

What is claimed is:
 1. A method of making an assay for detecting DNA amplified from a bisulfite-treated methylated marker DNA in the presence of DNA amplified from the bisulfite-treated unmethylated marker DNA that exhibits background methylation, the method comprising: a) isolating marker DNA from a first group of samples in which the marker DNA is unmethylated DNA exhibiting background methylation and from a second group of samples in which the marker DNA is methylated; b) treating the isolated marker DNA with bisulfite under conditions wherein unmethylated cytosine residues are converted to uracil residues to produce converted isolated marker DNA; c) processing the converted isolated marker DNA using a digital sequencing and/or digital polymerase chain reaction (PCR) to determine the methylation status for three or more CpG loci in each of at least 1000 individual copies of the isolated marker DNA from each of the first and second groups of samples; d) for each of the three or more CpG loci, determining a ratio between a mean methylation at that CpG locus in the first group of samples to a mean methylation at the corresponding CpG locus in the second group of samples; e) selecting a subset of at least three CpG loci for which the percentage of individual copies of the marker DNA from the first group of samples that are methylated at all of the at least three CpG loci in the subset is less than the percentage of individual copies of the marker DNA from the second group of samples that are methylated at all of the at least three CpG loci in the subset; and f) creating a DNA detection assay that detects the methylation status of all of the at least three CpG loci in the subset of CpG loci in a strand of the bisulfate-treated marker DNA, wherein the absence of methylation at any one of the CpG loci in the subset of CpG loci in a strand of the marker DNA produces an assay result classifying that strand of the marker DNA as not methylated, wherein creating the DNA detection assay comprises synthesizing at least one oligonucleotide complementary to a segment of marker DNA comprising the subset of CpG loci.
 2. The method of claim 1, wherein the methylation status for the at least three CpG loci is determined in each of at least 10,000 individual copies of the isolated marker DNA from each of the first and second groups of samples.
 3. The method of claim 1, wherein the methylation status for the at least three CpG loci is determined in each of at least 100,000 individual copies of the isolated marker DNA from each of the first and second groups of samples.
 4. The method of claim 1, wherein each of the first and second groups of samples comprises at least 10 samples.
 5. The method of claim 1, wherein each of the first and second groups of samples comprises at least 100 samples.
 6. The method of claim 1, wherein the subset of CpG loci comprises at least four CpG loci.
 7. The method of claim 1, wherein the subset of CpG loci comprises at least five CpG loci.
 8. The method of claim 1, wherein the DNA detection assay comprises at least one assay selected from the group consisting of a primer extension assay, a nucleic acid amplification assay, a structure-specific cleavage assay, 5′ nuclease cleavage assay, an invasive cleavage assay, and a ligation assay.
 9. A method of characterizing a test sample comprising: i) providing a DNA detection assay created according to claim 1, ii) isolating strands of DNA from a test sample and treating the strands of DNA from the test sample with bi sulfite under conditions wherein unmethylated cytosine residues are converted to uracil residues to produce strands of bi sulfite-treated marker DNA from the test sample, and iii) characterizing a set of at least three CpG loci in the strands of the bi sulfite-treated marker DNA from the test sample using the DNA detection assay to produce assay results classifying strands of the bi sulfite-treated marker DNA from the test sample as methylated or not methylated.
 10. The method of claim 9, wherein the set of CpG loci comprises at least four CpG loci.
 11. The method of claim 9, wherein the set of CpG loci comprises at least five CpG loci.
 12. The method of claim 9, wherein the test sample is a sample from a human.
 13. The method of claim 9, wherein the test sample from a human is a stool sample.
 14. The method of claim 9, wherein the at least one marker DNA comprises cancer and/or adenoma marker DNA.
 15. The method of claim 9, wherein the at least one marker DNA comprises at least three cancer or adenoma marker DNAs.
 16. The method of claim 9, wherein the at least one marker DNA comprises at least four cancer or adenoma marker DNAs.
 17. The method of claim 9, wherein the cancer or adenoma marker DNA is selected from the group consisting of vimentin, BMP3, Septin 9, TFPI2, 2 regions of LRAT, and EYA4 DNAs. 